Explorez tous les épisodes du podcast The Neil Ashton Podcast
| Titre | Date | Durée | |
|---|---|---|---|
| S4 EP5 - Prof. Nils Thuerey on Differentiable Physics and Foundation Models | 23 Jul 2026 | 01:14:47 | |
Differentiable physics, neural emulators and foundation models for PDEs are the focus of this conversation with Professor Nils Thuerey, head of the Physics-based Simulation group at TUM. Neil and Nils discuss PhiFlow, PICT, Tadpole, scalable 3D transformers, online synthetic data, open datasets, world models and agents that call physics simulators. Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s4-e5-prof-nils-thuerey-on-differentiable-physics-and-foundation-models/ Topics Differentiable physics and physics-based deep learning PhiFlow and differentiable simulation across ML frameworks When neural emulators can outperform their training data Foundation models for PDEs and synthetic online training Scalable 3D transformers and high-resolution simulations LES, temporal data and correlated CFD datasets Open-source tools, startups and physics-aware world models AI agents that call physics simulators Papers Neural Emulator Superiority: When Machine Learning for PDEs Surpasses its Training Data https://arxiv.org/abs/2510.23111 Tadpole: Autoencoders as Foundation Models for 3D PDEs with Online Learning https://arxiv.org/abs/2605.15284 P3D: Scalable Neural Surrogates for High-Resolution 3D Physics Simulations with Global Context https://arxiv.org/abs/2509.10186 PICT — A Differentiable, GPU-Accelerated Multi-Block PISO Solver for Simulation-Coupled Learning Tasks in Fluid Dynamics https://arxiv.org/abs/2505.16992 PhiFlow: Differentiable Simulations for PyTorch, TensorFlow and JAX https://proceedings.mlr.press/v235/holl24a.html Physics-based Deep Learning https://arxiv.org/abs/2109.05237 Learning to Control PDEs with Differentiable Physics https://arxiv.org/abs/2001.07457 Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers https://arxiv.org/abs/2007.00016 tempoGAN: A Temporally Coherent, Volumetric GAN for Super-resolution Fluid Flow https://arxiv.org/abs/1801.09710 Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations of Airfoil Flows https://arxiv.org/abs/1810.08217 WeatherBench: A Benchmark Dataset for Data-Driven Weather Forecasting https://arxiv.org/abs/2002.00469 SuperWing: A Comprehensive Transonic Wing Dataset for Data-Driven Aerodynamic Design https://arxiv.org/abs/2512.14397 Links Nils Thuerey and the Physics-based Simulation group https://ge.in.tum.de/about/n-thuerey/ Chapters 00:00 Podcast intro 00:39 Introducing Prof. Nils Thuerey 04:13 Conversation begins 05:13 From Computational Numerics to Graphics and Visual Effects 07:17 Physics-Based Deep Learning Before ChatGPT 10:01 CNNs, Graphics and the Move into Engineering Applications 12:37 PhiFlow and Differentiable Physics 14:13 Can Neural Emulators Surpass Their Training Data? 18:00 The Promise and Limits of Foundation Models for PDEs 20:43 Tadpole and Synthetic Online Pre-Training 24:07 From Canonical PDEs to Navier-Stokes and Industrial CFD 26:35 What Do Foundation Models Actually Learn? 28:36 PDE Pre-Training vs. Millions of CFD Simulations 33:08 Scaling 3D Transformers and Training Infrastructure 35:58 Generating and Training on Data in Real Time 38:00 LES, Temporal Data and Turbulence 42:15 Overfitting and Correlated Simulation Data 44:27 Bringing Differentiable Solvers Back into the Loop 45:31 WeatherBench, APEBench and the Value of Benchmarks 47:09 SuperWing, Open Datasets and Commercial Data 51:31 Open Source, Commercial Models and a Technical Oscar 56:17 Academia, Startups and Industry 01:00:55 What Will Change Over the Next Five Years? 01:02:07 World Models and the Need for Physics 01:08:19 Agents, Tool Use and Calling Physics Simulators 01:11:22 Career Advice for AI and Simulation 01:13:54 Closing Thoughts | |||
| S4 EP4 - Prof. Paola Cinnella on AI for Science and Fluid Mechanics | 09 Jul 2026 | 01:25:39 | |
RANS uncertainty, data-driven turbulence modeling and AI for Science are the focus of this conversation with Professor Paola Cinnella, Professor of Fluid Mechanics at Sorbonne University and Director of SCAI. Neil and Paola discuss high-order methods, dense gases, Bayesian uncertainty, AirfRANS, surrogate modeling, scientific publishing and education in the AI era. Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s4-e4-prof-paola-cinnella-on-ai-for-science-and-fluid-mechanics/ Topics Fluid mechanics, CFD and high-order schemes Dense gases, real-gas effects and expansion shockwaves Uncertainty quantification and Bayesian methods RANS turbulence-model uncertainty AirfRANS and CFD datasets for machine learning Turbulence modeling vs. surrogate modeling Scientific publishing and ML-for-CFD standards SCAI and AI for Science Education, ChatGPT and centaur scientists Papers Quantification of model uncertainty in RANS simulations: A review — Heng Xiao, Paola Cinnella https://doi.org/10.1016/j.paerosci.2018.10.001 Discovery of Algebraic Reynolds-Stress Models Using Sparse Symbolic Regression — Martin Schmelzer, Richard P. Dwight, Paola Cinnella https://doi.org/10.1007/s10494-019-00089-x Bayesian estimates of parameter variability in the k-epsilon turbulence model — W.N. Edeling, P. Cinnella, R.P. Dwight, H. Bijl https://doi.org/10.1016/j.jcp.2013.10.027 AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier-Stokes Solutions https://arxiv.org/abs/2212.07564 Data-driven turbulence modeling — Paola Cinnella https://arxiv.org/abs/2404.09074 Direct numerical simulations of supersonic turbulent channel flows of dense gases — Luca Sciacovelli, Paola Cinnella, Xavier Gloerfelt https://doi.org/10.1017/jfm.2017.237 Links Paola Cinnella named Director of SCAI https://scai.sorbonne-universite.fr/news/paola-cinnella-new-director SCAI https://scai.sorbonne-universite.fr/ Paola Cinnella — HAL publications https://cv.hal.science/paola-cinnella Paola Cinnella — Google Scholar https://scholar.google.com/citations?hl=fr&user=wBRA0JAAAAAJ ERCOFTAC SIG 54 — Machine Learning for Fluid Dynamics https://www.ercoftac.org/special_interest_groups/54-machine-learning-for-fluid-dynamics/master-of-science-internships/ Chapters 00:00 Podcast intro 00:39 Introducing Prof. Paola Cinnella 03:28 Conversation begins 03:56 How Paola Found Fluid Mechanics 07:09 Moving from Italy to France 08:37 High-Order Schemes and Compressible Flows 09:30 Building an Academic Career 12:06 Dense Gases and Uncertainty Quantification 15:16 Expansion Shockwaves and Real-Gas Effects 19:17 Returning to Paris and Academic Mobility 24:52 Academia, Passion and Persistence 27:51 Bayesian Methods and Turbulence Uncertainty 30:47 Learning Statistics Across Disciplines 33:07 LearnFluidS, AirfRANS and CFD Datasets 36:33 Skepticism and Physics in ML Turbulence Modeling 40:41 Could ML Lead to a Universal Turbulence Model? 42:59 Turbulence Models, Surrogate Models and RANS 45:03 Why LES Alone Cannot Solve Optimization 47:15 Multi-Fidelity Modeling 49:08 What Computers & Fluids Looks for in ML-for-CFD Papers 54:05 CFD Metrics vs. Machine-Learning Metrics 57:13 Overselling, Publication Pressure and Quality 01:02:22 SCAI and AI for Science 01:06:07 Cross-Disciplinary AI for Science 01:09:26 Education in the AI Era 01:12:44 Critical Thinking and AI Outputs 01:18:15 AI as a Companion, Not a Replacement 01:21:42 AlphaFold and the Future of Discovery 01:23:43 Training Centaur Scientists 01:25:11 Closing Thoughts | |||
| S3 EP4 - 5 tips for CAE engineers in the era of AI | 02 Sep 2025 | 00:23:48 | |
In this episode of the Neil Ashton podcast, Neil discusses the impact of AI on CAE engineering, providing five essential tips for engineers to thrive in this evolving landscape. The conversation covers the importance of maintaining an open mind, continuous education, and preparing for AI physics applications. It also delves into the build vs. buy dilemma for AI solutions and the emerging concept of agentic AI, which promises to revolutionize engineering practices. Chapters 00:00 Introduction to the Podcast and AI in Engineering 01:03 Five Tips for CAE Engineers in the Era of AI 01:24 1: Keeping an Open Mind 07:39 2: Understanding AI Physics and Its Applications 13:30 3: Preparing for AI Implementation in Engineering 18:54 4: The Build vs. Buy Dilemma in AI Solutions 22:20 5: The Future of Agentic AI in Engineering Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s3-e4-five-tips-for-cae-engineers-in-the-era-of-ai/ | |||
| S3 EP3 - Professor Johannes Brandstetter on AI for Computational Fluid Dynamics | 19 Aug 2025 | 01:18:02 | |
In this conversation, Neil Ashton interviews Prof. Johannes Brandstetter, a physicist turned machine learning expert, about his journey from academia to industry, focusing on the application of machine learning in engineering and computational fluid dynamics (CFD). They discuss the Aurora project, the challenges of integrating machine learning with engineering, and the importance of data in training models. Johannes shares insights on the use of transformers in modeling, the significance of resolution independence, and the role of open-source practices in advancing the field. The conversation also touches on the challenges of founding a startup and the need for multidisciplinary collaboration in tackling complex engineering problems. AB-UPT transform paper: https://arxiv.org/abs/2502.09692 Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s3-e3-prof-johannes-brandstetter-on-ai-for-computational-fluid-dynamics/ | |||
| S3 EP2 - Prof. Russell Cummings - World leader in Aerospace Engineering and Hypersonics | 05 Aug 2025 | 01:43:14 | |
In this episode of the Neil Ashton podcast, Professor Russell Cummings shares his extensive journey through the fields of aerodynamics, computational fluid dynamics and hypersonics. He discusses his early inspirations, his early days at University and the Hughes Aircraft Company - a key time during this life. He also talks about the cyclical nature of hypersonics research, and the challenges faced in computational fluid dynamics (CFD). Prof. Cummings emphasizes the importance of perseverance in engineering careers and the need for collaboration between experimental and computational methods. He also shares insights on the role of AI in hypersonics and offers valuable advice for aspiring engineers. Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s3-e2-prof-russell-cummings-aerospace-engineering-and-hypersonics/ | |||
| S3 EP1 - Prof. Mike Giles - A CFD and Computational Finance Pioneer | 21 Jul 2025 | 02:07:11 | |
In this episode of the Neil Ashton podcast, Professor Mike Giles shares his extensive journey through the fields of computational fluid dynamics (CFD), computational finance and HPC. He discusses his early academic influences, his early days at Cambridge, internships at Rolls-Royce, his transition to MIT and Oxford where he made significant contributions to high-performance computing and numerical analysis. The conversation highlights his hands-on approach to research and teaching, as well as his pioneering work in Monte Carlo methods and GPU computing. This conversation explores the journey of a mathematician and engineer from MIT to Rolls-Royce and then to Oxford, highlighting the evolution of computational engineering, the development of the Hydra code, and the transition from CFD to financial applications. In this conversation, the speaker reflects on their journey through burnout, career transitions, and the evolution of their work in computational finance and numerical analysis. They discuss the challenges of managing large software projects, the shift from Hydra code development to finance, and the integration of advanced methodologies in their work. The conversation also touches on the role of high-performance computing, the impact of AI on research, and advice for the next generation of students pursuing careers in mathematics and programming.
Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s3-e1-prof-mike-giles-a-cfd-and-computational-finance-pioneer/ | |||
| S2 EP11 - Foundational AI Models for Fluids | 24 Apr 2025 | 00:22:33 | |
In this episode of the Neil Ashton podcast, the discussion revolves around foundational models in fluid dynamics, particularly in the context of computational fluid dynamics (CFD). Neil shares insights from a recent panel discussion and explores the potential of AI in predicting fluid behavior. He discusses the evolution of AI in CFD, the challenges of data availability, and the differing adoption rates between industries. The episode concludes with predictions about the future of foundational models and their impact on the engineering landscape. Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s2-e11-foundational-ai-models-for-fluids/ | |||
| S2 EP10 - Dr. Kurt Bergin-Taylor, Head of Innovation - Tudor Pro Cycling | 10 Mar 2025 | 01:01:05 | |
In this episode of the Neil Ashton podcast, Neil discusses the intersection of cycling and engineering with Kurt Bergin-Taylor, head of innovation at Tudor Pro Cycling. They explore how technology and science are transforming cycling into a more competitive and innovative sport, akin to Formula One. The conversation covers various aspects of cycling, including the importance of aerodynamics, nutrition, and the holistic approach to rider performance. Kurt shares insights from his academic background and experiences in professional cycling, emphasizing the need for tailored training and the integration of technology in enhancing performance. They discuss the future of cycling innovation, emphasizing the importance of individualization in gear, collaborative relationships with partners, and the evolving mindset of young cyclists. Kurt highlights the significance of data and AI in optimizing performance and strategies in cycling, while also addressing the need for viewer engagement in the sport. Finally Kurt shares valuable advice for aspiring engineers looking to enter the cycling industry, stressing the importance of mentorship and practical experience. Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s2-e10-dr-kurt-bergin-taylor-head-of-innovation-tudor-pro-cycling/ | |||
| S2, EP9 - New Job Update! (and a small apology..) | 21 Feb 2025 | 00:08:46 | |
A short episode to give a brief update on what I've been doing and to say sorry for not putting out episodes recently. I've joined NVIIDA as a Distinguished CAE Architect and have been rather busy! New episodes will be coming soon! Listen to the episode to learn more. Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s2-e9-new-job-update/ | |||
| S2, EP8 - Neil Ashton - Career advice for Engineers | 09 Jan 2025 | 01:00:09 | |
In this episode of the Neil Ashton podcast, Neil discusses career advice for aspiring engineers, focusing on the differences between various types of companies, job roles, and the growing importance of software skills in the engineering field. The conversation highlights the pros and cons of working in large enterprises, startups, and consulting firms, as well as the diverse career paths available beyond traditional engineering roles. In this conversation, Neil discusses the evolving landscape of engineering careers, particularly focusing on the increasing relevance of software development and the tech sector. He highlights the diverse career paths available within tech, including software development, product management, and solution architecture, as well as the growing importance of AI in engineering. Neil emphasizes the opportunities for engineers to transition into tech roles and the need for a strong understanding of the tech ecosystem to navigate career decisions effectively. Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s2-e8-neil-ashton-career-advice-for-engineers/ | |||
| S2, EP7 - Prof. Michael Mahoney - Perspectives on AI4Science | 26 Dec 2024 | 01:16:44 | |
In this episode of the Neil Ashton podcast, Professor Michael Mahoney discusses the intersection of machine learning, mathematics, and computer science. The conversation covers topics such as randomized linear algebra, foundational models for science, and the debate between physics-informed and data-driven approaches. Prof. Mahoney shares insights on the relevance of his research, the potential of using randomness in algorithms, and the evolving landscape of machine learning in scientific disciplines. He also discusses the evolution and practical applications of randomized linear algebra in machine learning, emphasizing the importance of randomness and data availability. He explores the tension between traditional scientific methods and modern machine learning approaches, highlighting the need for collaboration across disciplines. Prof Mahoney also addresses the challenges of data licensing and the commercial viability of machine learning solutions, offering insights for aspiring researchers in the field. Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s2-e7-prof-michael-mahoney-perspectives-on-ai-for-science/ | |||
| S2, EP6 - Dr. Prith Banerjee - ANSYS CTO | 16 Dec 2024 | 01:10:37 | |
In this episode of the Neil Ashton Podcast, Dr. Prith Banerjee, CTO of Ansys, shares his extensive journey from academia to the corporate world, discussing the interplay between academia and industry, the role of startups in innovation, and the transformative potential of AI and ML in simulation. He emphasizes the importance of solving real-world problems and the need for collaboration between academia, startups, and large corporations to foster disruptive innovation. He discusses innovative business models for data sharing, the intersection of data-driven and physics-informed approaches, the role of open source in AI innovation, the potential of foundational models in computer-aided engineering (CAE), the future of quantum computing in simulation, and offers advice for aspiring innovators and entrepreneurs. He emphasizes the importance of collaboration, data governance, and the need for interdisciplinary approaches to solve complex problems in engineering and technology. Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s2-e6-dr-prith-banerjee-ansys-cto/ | |||
| S4 EP3 - Prof. Ricardo Vinuesa on AI for Fluid Mechanics | 25 Jun 2026 | 01:05:48 | |
Foundation models, explainable AI and autonomous discovery in fluid mechanics are the focus of this conversation with Professor Ricardo Vinuesa, Associate Chair for Research and Associate Professor of Aerospace Engineering at the University of Michigan. Neil and Ricardo discuss latent representations, turbulence, reduced-order modeling, flow control and whether AI can discover physical mechanisms that humans might miss. Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s4-e3-prof-ricardo-vinuesa-on-ai-for-fluid-mechanics/ Topics Can fluid mechanics have a “ChatGPT moment”? Foundation models and latent representations for turbulent flows Explainable AI, causality and identifying the mechanisms that matter Why classical coherent structures may tell only part of the turbulence story Physics-informed vs purely data-driven machine learning Reduced-order modeling, autoencoders, transformers and nonlinear compression Deep reinforcement learning for flow control and optimization Agentic AI and autonomous scientific discovery in PDE-governed systems How academia, computer science and engineering education must adapt to AI Papers Agentic Exploration of PDE Spaces using Latent Foundation Models for Parameterized Simulations — Abhijeet Vishwasrao et al. https://arxiv.org/abs/2604.09584 Multi-agent LLMs and latent foundation models autonomously explore flow physics in a tandem-cylinder problem. Enhancing computational fluid dynamics with machine learning — Ricardo Vinuesa, Steven L. Brunton https://doi.org/10.1038/s43588-022-00264-7 A roadmap for useful ML in CFD, including faster simulations, turbulence models and reduced-order models. Identifying regions of importance in wall-bounded turbulence through explainable deep learning — Andrés Cremades et al. https://doi.org/10.1038/s41467-024-47954-6 Explainable AI identifies flow structures that matter for prediction and control. β-Variational autoencoders and transformers for reduced-order modelling of fluid flows — Alberto Solera-Rico et al. https://doi.org/10.1038/s41467-024-45578-4 Disentangled latent spaces, autoencoders and transformers support interpretable reduced-order models. Improving turbulence control through explainable deep learning — Miguel Beneitez et al. https://arxiv.org/abs/2504.02354 Explainable AI and deep reinforcement learning target turbulence-sustaining mechanisms. Links VinuesaLab https://www.vinuesalab.com/ Ricardo Vinuesa — University of Michigan Aerospace Engineering https://aero.engin.umich.edu/people/ricardo-vinuesa/ AI and ML for Fluid Dynamics course — Ricardo Vinuesa and Sergio Hoyas https://www.flowthermolab.com/courses/ai-ml-for-fluids/ VinuesaLab YouTube channel https://www.youtube.com/@VinuesaLab AI for Fluid Mechanics, Sustainability & XAI — Ricardo Vinuesa https://www.youtube.com/watch?v=TOfwf4ffPnU Modelling and controlling turbulent flows through deep learning — Ricardo Vinuesa https://www.youtube.com/watch?v=0AOY_agZ8WM Chapters 00:00 Podcast intro 03:20 The Evolution of Foundation Models in Fluid Dynamics 10:22 Understanding Explainable AI in Fluid Mechanics 15:34 Challenges in Data Fidelity for Foundation Models 20:29 Machine Learning vs. Reduced-Order Modeling 24:22 The Shift from Turbulence Modeling to Surrogate Models 29:48 Exploring Agentic Systems for Scientific Discovery 37:21 Exploring Latent Representations in Fluid Dynamics 40:40 The Role of AI in Autonomous Discovery 41:57 Bridging Fluid Mechanics and Computer Science 45:28 Data-Driven vs. Physics-Driven Models 51:34 The Role of Academia in AI and Fluid Mechanics 56:27 Optimization and Control in Machine Learning 01:00:28 The Future of AI in Fluid Dynamics: Beyond ChatGPT | |||
| S2, EP5 - NASA's Quesst for Quieter Supersonic Flight with Peter Coen | 04 Dec 2024 | 01:15:14 | |
In this episode of the Neil Ashton podcast, Peter Coen from NASA discusses the evolution and future of supersonic travel, focusing on the challenges faced by the Concorde, the technological hurdles of modern supersonic aircraft, and the innovative NASA Quesst mission (and X-59 demonstrator) that aims to provide crucial data to rewrite the aviation noise regulations. The conversation delves into the history of supersonic flight, the impact of sonic booms, and the regulatory landscape that will shape the future of aviation. In this conversation, Peter discusses the complexities of supersonic flight, focusing on the physics of shockwaves, innovative design strategies to mitigate sonic booms, and advancements in pilot visibility technology. He emphasizes the importance of human factors in aircraft design and the role of simulation in the development process. The discussion also covers the challenges of engine technology for commercial supersonic travel, the potential for hypersonic passenger travel, and the future of battery technology in aviation. Finally, Peter offers career advice for aspiring professionals in the aeronautics field. Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s2-e5-nasa-s-quesst-for-quieter-supersonic-flight-with-peter-coen/ | |||
| S2, EP4 - Celebrating Prof. Antony Jameson: A CFD Pioneer | 20 Nov 2024 | 02:11:34 | |
In this episode of the Neil Ashton podcast, we celebrate the life and contributions of Professor Antony Jameson, a pioneer in Computational Fluid Dynamics (CFD). The conversation explores his early influences, academic journey, and significant contributions to aerodynamics and engineering. Professor Jameson shares insights from his career in both academia and industry, highlighting pivotal moments that shaped his work in CFD and transonic flow. Prof. Jameson discusses his journey through the complexities of numerical methods for fluid flow, his transition from industry to academia, the development of influential flow codes, and the evolution of computational fluid dynamics (CFD). He reflects on the challenges of teaching, the impact of his work on the aerospace industry, and the commercialization of CFD technologies. In this conversation, he shares his journey from academia to industry, discussing the challenges and successes he faced in the field of aerodynamics and computational fluid dynamics. He reflects on the importance of innovation, the impact of industry experience on academic research, and offers valuable advice for aspiring professionals in aeronautics. The discussion also touches on the evolution of computational power and the role of machine learning in the field. Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s2-e4-celebrating-prof-antony-jameson-a-cfd-pioneer/ | |||
| S2, EP3 - Dr Michael Hutchinson - Cycling aerodynamics and the lifelong pursuit to go faster | 08 Nov 2024 | 01:38:00 | |
In this episode of the Neil Ashton podcast, we delve into the fascinating world of cycling, focusing on the critical role of aerodynamics and the evolution of training techniques. Featuring Dr. Michael Hutchinson, a former top-level cyclist and expert in cycling aerodynamics, the conversation explores Dr. Hutch's journey from competitive cycling to becoming a prominent figure in cycling media. The discussion highlights the importance of power meters in training, the cultural landscape of cycling in the UK, and the technical innovations that have transformed the sport. In this conversation, we discuss the evolution of cycling performance, focusing on the impact of training, nutrition, and equipment. We highlight the importance of training less, the advancements in nutrition that allow cyclists to perform better, and the diverse training approaches that exist among athletes. The conversation also touches on the professionalism of cyclists, the rise of women's cycling, and the significant role of aerodynamics and equipment in enhancing performance. In this conversation, Neil and Dr Hutch discusses the intricate balance between power and aerodynamics in cycling, the evolution of rider trust in aerodynamic advice, and the significant impact of wind tunnels on performance. He explores the challenges of wind tunnel testing versus real-world validation, the role of computational fluid dynamics (CFD) in cycling aerodynamics, and the regulatory challenges that arise with advancing technology. Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s2-e3-dr-michael-hutchinson-cycling-aerodynamics-and-the-lifelong-pursuit-to-go-faster/ | |||
| S2, EP2: The Future of CFD: 5 Key Trends to Watch | 24 Oct 2024 | 00:46:26 | |
In this episode, Neil discusses five key trends in Computational Fluid Dynamics (CFD) that are shaping the industry now and in the coming years. He emphasizes the growing importance of GPUs, the integration of AI and machine learning, the shift towards cloud computing, and the potential for mergers and acquisitions in the CFD space. Each trend is explored in detail, highlighting its implications for accuracy, efficiency, and the future of simulation technologies. Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s2-e2-the-future-of-cfd-five-key-trends-to-watch/ | |||
| S2, EP1 - Dr. Nikolas Tombazis - From Poacher to Gamekeeper, Defining the future of Formula 1 | 10 Oct 2024 | 01:12:15 | |
In this episode of the Neil Ashton podcast, Nikolas Tombazis discusses his journey into engineering and Formula One, starting from his passion for mathematics, physics, and design. He shares how his childhood dream of designing Formula One cars led him to pursue engineering. Tombazis also talks about his experience at Cambridge University and the freedom he enjoyed during his university years. He then delves into his decision to pursue a PhD in experimental aerodynamics and the valuable skills he gained from his research. Tombazis reflects on the challenges and responsibilities of being a chief aerodynamicist in Formula One, as well as the evolving role of CFD in the industry. The conversation explores the advancements in wind tunnel technology and computational fluid dynamics (CFD) in Formula One. It discusses the role of CFD as a design tool and the potential for it to become the predominant tool in the future. The conversation also touches on the balance between the technical aspects of the sport and the entertainment value for fans. The importance of teamwork, leadership, and culture in Formula One teams is highlighted, as well as the challenges of maintaining success and avoiding complacency. The conversation concludes with advice for aspiring Formula One professionals, emphasizing the value of a broad skill set and the potential for Formula One as a stepping stone to other industries. Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s2-e1-dr-nikolas-tombazis-defining-the-future-of-formula-1/ | |||
| S1, EP14 - Season 1 Recap and what's next | 08 Aug 2024 | 00:20:09 | |
The first season of the Neil Ashton podcast comes to a close with a recap of the episodes and a glimpse into what's to come in the next season. Look out for Season 2 in September with lots more great guests and discussion on hypersonics, CFD, Formula One, cycling, space exploration and more! Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s1-e14-season-1-recap-and-what-comes-next/ | |||
| S1, EP13 - Prof. Anima Anandkumar - The future of AI+Science | 30 Jul 2024 | 01:06:56 | |
Professor Anima Anandkumar is one of the world’s leading scientists in AI and machine learning, with more than 30,000 citations, an h-index of 80 and landmark papers including FourCastNet, which received worldwide coverage for demonstrating how AI can accelerate weather prediction. She is the Bren Professor at Caltech, leads the AI+Science Lab and previously served as Senior Director of AI Research at NVIDIA. In this episode I speak to her about her background in academia and industry, her journey into machine learning, and the importance of AI for science. We discuss the integration of AI and scientific research, the potential of AI in weather modeling, and the challenges of applying AI to other areas of science. Prof Anandkumar shares examples of successful AI applications in science and explains the concept of AI + science. We also touch on the skepticism surrounding machine learning in physics and the need for data-driven approaches. The conversation explores the potential of AI in the field of science and engineering, specifically in the context of physics-based simulations. Prof. Anandkumar discusses the concept of neural operators, highlights the advantages of neural operators, such as their ability to handle multiple domains and resolutions, and their potential to revolutionize traditional simulation methods. She emphasizes the importance of combining AI with scientific knowledge and traditional numerical solvers, supported by collaboration between machine-learning specialists and domain experts. Finally, she offers advice for PhD students and highlights the value of smaller workshops and conferences for following emerging ideas. Links: LinkedIn: https://www.linkedin.com/in/anima-anandkumar/ Ted Video: https://www.youtube.com/watch?v=6bl5XZ8kOzI FourCastNet: https://arxiv.org/abs/2202.11214 Google Scholar: https://scholar.google.com/citations?hl=en&user=bEcLezcAAAAJ Lab page: http://tensorlab.cms.caltech.edu/users/anima/ Takeaways - Anima's background includes both academia and industry, and she sees value in bridging the gap between the two. - AI for science is the integration of AI and scientific research, with the goal of enhancing and accelerating scientific developments. - AI has shown promise in weather modeling, with AI-based weather models outperforming traditional numerical models in terms of speed and accuracy. - The skepticism surrounding machine learning in physics can be addressed by verifying the accuracy of AI models against known physics principles. - Applying AI to other areas of science, such as aircraft design and fluid dynamics, presents challenges in terms of data availability and computational cost. Neural operators have the potential to revolutionize traditional simulation methods in science and engineering. - Integrating AI with scientific knowledge is crucial for the development of effective AI models in the field of physics-based simulations. - Interdisciplinary collaboration between ML specialists and domain experts is essential for advancing AI in science and engineering. - The future of AI in science and engineering lies in the integration of various modalities, such as text, observational data, and physical understanding. Chapters 00:00 Introduction and Overview 04:29 Professor Anima Anandkumar's Career Journey 09:14 Moving to the US for PhD and Transitioning to Industry 13:00 Academia vs Industry: Personal Choices and Opportunities 17:49 Defining AI for Science and Its Importance 22:05 AI's Promise in Enhancing Scientific Discovery 28:18 The Success of AI-Based Wea Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s1-e13-prof-anima-anandkumar-the-future-of-ai-and-science/ | |||
| S1, EP12 - Prof Karthik Duraisamy - Scientific Foundation Models | 23 Jul 2024 | 01:32:39 | |
Prof. Karthik Duraisamy is a Professor at the University of Michigan, the Director of the Michigan Institute for Computational Discovery and Engineering (MICDE) and the founder of the startup Geminus.AI. In this episode, we discuss AI for Science, with a particular focus on fluid dynamics and computational fluid dynamics. Prof. Duraisamy talks about the progress and challenges of using machine learning in turbulence modeling and the potential of surrogate models (both data-driven and physics-informed neural networks). He also explores the concept of foundation models for science and the role of data and physics in AI applications. The discussion highlights the importance of using machine learning as a tool in the scientific process and the potential benefits of large language models in scientific discovery. We also discuss the need for collaboration between academia, tech companies, and startups to achieve the vision of a new platform for scientific discovery. Prof. Duraisamy predicts that in the next few years, there may be major advancements in foundation models for science; however, he cautions against unrealistic expectations and emphasizes the importance of understanding the limitations of AI. Links: Summer school tutorials https://github.com/scifm/summer-school-2024 (scroll down for links to specific tutorials) SciFM24 recordings : https://micde.umich.edu/news-events/annual-symposia/2024-symposium/ SciFM24 Summary : https://drive.google.com/file/d/1eC2HJdpfyZZ42RaT9KakcuACEo4nqAsJ/view Trillion parameter consortium : https://tpc.dev Turbulence Modelling in the age of data: https://www.annualreviews.org/content/journals/10.1146/annurev-fluid-010518-040547 LinkedIn: https://www.linkedin.com/showcase/micde/ Chapters 00:00 Introduction 09:41 Turbulence Modeling and Machine Learning 21:30 Surrogate Models and Physics-Informed Neural Networks 28:42 Foundation Models for Science 35:23 The Power of Large Language Models 47:43 Tools for Foundation Models 48:39 Interfacing with Specialized Agents 53:31 The Importance of Collaboration 58:57 The Role of Agents and Solvers 01:08:26 Balancing AI and Existing Expertise 01:21:28 Predicting the Future of AI in Fluid Dynamics 01:23:18 Closing Gaps in Turbulence Modeling 01:25:42 Achieving Productivity Benefits with Existing Tools Takeaways -Machine learning is a valuable tool in the development of turbulence modeling and other scientific applications. -Data-driven modeling can provide additional insights and improve the accuracy of scientific models. -Physics-informed neural networks have potential in solving inverse problems but may not be as effective in solving complex PDEs. -Foundation models for science can benefit from a combination of data-driven approaches and physics-based knowledge. -Large language models have the potential to assist in scientific discovery and provide valuable insights in various scientific domains. Having a strong foundation in the domain of study is crucial before applying AI techniques. -Collaboration between academia, tech companies, and startups is necessary to achieve the vision of a new platform for scientific discovery. -Understanding the limitations of AI and managing expectations is important. -AI can be a valuable tool for productivity gains and scientific assistance, but it will not replace human expertise. Keywords #computationalfluiddynamics , #ailearning #largelanguagemodels , #cfd , #supercomputing , #fluiddynamics Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s1-e12-prof-karthik-duraisamy-scientific-foundation-models/ | |||
| S1, EP11 - Prof. Max Welling - Machine Learning Pioneer & AI4Science Visionary | 09 Jul 2024 | 01:07:33 | |
In this episode, Neil interviews Professor Max Welling, one of the foremost experts in Machine Learning about AI4Science: the use of machine learning and AI to solve challenges in various scientific disciplines. They discuss and debate between data-driven and physics-driven approaches, the potential for foundational models, the importance of open sourcing models and data, the challenges of data sharing in science, and the ethical considerations of releasing powerful models. The conversation covers the role of academia, industry, and startups in driving innovation, with a focus on the field of AI. Professor Welling discusses the advantages and limitations of each sector and shares his experience in academia, big tech companies, and startups. The conversation then shifts to Professor Wellings new company; CuspAI, which focuses on material discovery for carbon capture using metal organic frameworks and machine learning. Prof. Welling provides insights into the potential applications of this technology and the importance of addressing sustainability challenges. The conversation concludes with a discussion on career advice and the future of AI for science. Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s1-e11-prof-max-welling-machine-learning-pioneer-and-ai-for-science-visionary/ | |||
| S1, EP10 - AI4Science - Personal Thoughts and Perspectives | 02 Jul 2024 | 00:20:03 | |
This episode sets the scene for upcoming discussions on AI4Science with world renowned experts on machine learning. The focus is on using machine learning to solve scientific problems, such as computational fluid dynamics, weather modeling, material design, and drug discovery. The episode introduces the concept of machine learning and its potential to accelerate simulations and predictions. The episode also discusses the differences between machine learning for scientific problems and large language models, and the ongoing debate on incorporating physics into machine learning models. Chapters 00:00 Podcast intro 00:30 Introduction: AI for Science and Machine Learning 02:29 The Importance of Computational Fluid Dynamics 04:53 The Limitations of Physical Testing and Simulation 05:53 Accelerating Simulations and Predictions with Machine Learning 09:51 Data-Driven vs Physics-Informed Approaches in Machine Learning 13:10 The Future of Machine Learning in Science: Foundational Models Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s1-e10-ai-for-science-personal-thoughts-and-perspectives/ | |||
| S4 EP2 - Prof. Nathan Kutz on Physics-Informed AI and Data-Driven Modeling | 11 Jun 2026 | 01:17:23 | |
Physics-informed AI, DMD, SINDy and data-driven engineering are the focus of this conversation with Professor J. Nathan Kutz, Director of Physics-Informed AI at Autodesk. Neil and Nathan trace machine learning’s evolution in engineering, the role of physics in trustworthy models, and the future of autonomous agents, design automation and human expertise. Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s4-e2-prof-nathan-kutz-on-physics-informed-ai-and-data-driven-modeling/ Topics History of machine learning in engineering Dynamic Mode Decomposition (DMD) and Sparse Identification of Nonlinear Dynamics (SINDy) Physics-informed AI and reduced-order modeling The debate between physics-based and data-driven models The future of autonomous agents and their impact on industry Papers Flower discrimination by pollinators in a dynamic chemical environment — Jeffrey A. Riffell, Eli Shlizerman, Elischa Sanders, Leif Abrell, Billie Medina, Armin J. Hinterwirth, J. Nathan Kutz https://doi.org/10.1126/science.1251041 Nathan’s early move into neuroscience and data-driven biological modeling. Data assimilation and discrepancy modeling with shallow recurrent decoders — Yuxuan Bao, J. Nathan Kutz https://arxiv.org/abs/2512.01170 Using ML to close the gap between simulation and reality. Discovering governing equations from data by sparse identification of nonlinear dynamical systems — Steven L. Brunton, Joshua L. Proctor, J. Nathan Kutz https://doi.org/10.1073/pnas.1517384113 The foundational paper introducing SINDy. On Dynamic Mode Decomposition: Theory and Applications — Jonathan H. Tu, Clarence W. Rowley, Dirk M. Luchtenburg, Steven L. Brunton, J. Nathan Kutz https://doi.org/10.3934/jcd.2014.1.391 A key reference for Dynamic Mode Decomposition. Data-driven discovery of partial differential equations — Samuel H. Rudy, Steven L. Brunton, Joshua L. Proctor, J. Nathan Kutz https://doi.org/10.1126/sciadv.1602614 Extends equation discovery to PDEs and physical systems. Deep learning for universal linear embeddings of nonlinear dynamics — Bethany Lusch, J. Nathan Kutz, Steven L. Brunton https://doi.org/10.1038/s41467-018-07210-0 Connects deep learning with Koopman theory. Articraft: An Agentic System for Scalable Articulated 3D Asset Generation — Matt Zhou, Ruining Li, Xiaoyang Lyu, Zhaomou Song, Zhening Huang, Chuanxia Zheng, Christian Rupprecht, Andrea Vedaldi, Shangzhe Wu https://arxiv.org/abs/2605.15187 A practical example of agentic AI for engineering design. Links Articraft project page https://articraft3d.github.io/ Chapters 00:00 Podcast intro 00:40 Introduction to Episode 05:00 Welcoming Prof. Kutz 10:34 The Evolution of Data-Driven Modeling 16:13 Understanding the SINDy Algorithm and Its Implications 22:14 Comparing Reduced-Order Modeling and Modern Machine Learning 28:29 The Role of Data in Machine Learning and Physics 34:23 Challenges in Extrapolation and Real-World Applications 40:46 Insights from McLaren and Team Dynamics 46:07 The Shift from Academia to Industry 48:53 Collaboration and Innovation in Engineering 51:57 The Role of Human Expertise in Design 54:45 Leveraging AI in Formula One 57:32 The Future of AI and Workforce Dynamics 59:06 Navigating Career Choices in a Changing Landscape 01:03:02 The Evolution of Thought in Engineering 01:09:06 Preparing for the Future of Technology 01:14:04 Responsible Use of AI in Engineering | |||
| S1, EP9 - Dr Chris Rumsey - NASA & Computational Fluid Dynamics (CFD) | 25 Jun 2024 | 00:54:17 | |
In this episode of the Neil Ashton podcast, Neil interviews Dr. Chris Rumsey, Research Scientist at NASA Langley Research Center. Chris is one of the main CFD experts at NASA Langley is globally reconised as a leader in CFD, particularly for aeronautical applications. The conversation focuses on computational fluid dynamics (CFD) and turbulence modeling. They discuss Chris's career, his role in public dissemination of CFD methods, and his involvement in the Turbulence Modeling website. They also explore the High Lift Prediction Workshop and the role of machine learning in CFD and turbulence modeling. The conversation provides insights into working at NASA and the challenges and advancements in CFD and turbulence modeling. In this conversation, Neil and Chris Rumsey discuss the progress and challenges in solving the problem of high-lift aerodynamics in aircraft design. They explore the concept of certification by analysis and the role of computational fluid dynamics (CFD) in reducing the need for expensive wind tunnel and flight tests. They also delve into the use of machine learning in CFD and the challenges of reproducibility. The conversation then shifts to conferences, with Neil and Chris sharing their experiences and favorite events. They conclude by discussing career advice for aspiring aerospace professionals and the unique aspects of working at NASA. Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s1-e9-dr-chris-rumsey-nasa-and-computational-fluid-dynamics/ | |||
| S1, EP8 - Prof Jack Dongarra - High Performance Computing (HPC) Pioneer | 18 Jun 2024 | 00:53:49 | |
In this episode, Neil speaks to Professor Jack Dongarra, a renowned figure in the supercomputing and high-performance computing (HPC) world. He is a Professor at University of Tennessee as well as a Distinguished Researcher at Oak Ridge National Laboratory (ORNL) and a Turing Fellow at the University of Manchester. He is the inventor of the LINPACK library that is still used today to benchmark the Top 500 list of the most powerful supercomputers and was one of the key people involved in the creation of Message-Passing-Inferface (MPI). They discuss what is HPC, the challenges and opportunities in the field, and the future of HPC. They also touch on the role of machine learning and AI in HPC, the competitiveness of the United States in the field, and potential future technologies in HPC. Professor Dongarra shares his insights and advice based on his extensive experience in the field. Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s1-e8-prof-jack-dongarra-high-performance-computing-pioneer/ | |||
| S1, EP7 - Pat Symonds - Formula 1 Legend | 11 Jun 2024 | 01:21:51 | |
In this episode, Neil interviews Pat Symonds, one of the most well known and respected engineers in Formula One. They discuss Pat's career in engineering, his time in Formula One, and the evolution of the sport. Pat shares insights into his early motivations, his work with different teams, and the challenges he faced. They also touch on the growth of Motorsport Valley in the UK and the potential for Formula One teams to be based in other countries. In this conversation, Pat discusses his experience in Formula One and the challenges of being a technical director. He emphasizes the importance of continuous learning and the ability to make compromises in order to achieve success. He shares insights into the culture at Williams and Benetton and how it impacted their success. Additionally, he discusses the future of Formula One, including the use of AI and ML, the potential shift towards sustainable fuels, and the role of motor manufacturers. Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s1-e7-pat-symonds-formula-1/ | |||
| S1, EP6 - Prof Juan Alonso - the Future of Computational Science | 04 Jun 2024 | 01:27:06 | |
In this episode I speak to Prof Juan J. Alonso on his vision of the future of computational science as well as his journey from academia to entrepreneurship - founding Luminary Cloud. He reflects on the revolutions in computational science and the different ways of developing software throughout his career. Alonso emphasizes the importance of academia in creating and perpetuating knowledge, as well as the value of innovation and new ideas. He also discusses the changes in the CFD world, the emergence of new technologies like GPU computing and cloud computing, and the potential for advancements in computational simulations for analysis and design. We also touch on the transition of the aerospace industry towards commercial software and the potential for cloud computing to revolutionize CFD. The conversation concludes with a discussion on the progress made towards achieving the goals outlined in the 2030 CFD vision report and the role of machine learning and AI in simulation-driven workflows. Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s1-e6-prof-juan-alonso-the-future-of-computational-science/ | |||
| S1, EP5 - Dimitris Katsanis - Designing the World's Fastest Bikes | 28 May 2024 | 01:52:17 | |
In this conversation, Neil interviews Dimitris Katsanis, one of the world leading experts in bike design. They discuss the UCI regulations that govern bike design for road and track racing. Dimitris explains the evolution of bike design and the role of carbon fiber and titanium in creating lightweight and aerodynamic bikes. He also talks about his collaboration with Pinarello and the development of the Dogma F8 and F10 bikes. Dimitris emphasizes the importance of balancing weight, stiffness, and aerodynamics in bike design and the ongoing pursuit of improvement in the field. In this part of the conversation, Dimitris Katsanis discusses the evolution of bike design, the importance of aerodynamics and system drag reduction, the differences between track and road bike design, the interactions between the bike and rider, the impact of weight and aerodynamics in solo breakaways, the ongoing weight vs. aero debate, the role of stiffness in bike design, the relationship between stiffness and comfort in bike frames, and the potential of 3D printing and additive manufacturing in bike manufacturing. In this conversation, we also discuss the limitations of carbon fiber in bike design and the potential of 3D printing to overcome these limitations. He explains how 3D printing allows for the creation of custom shapes and internal structures that can improve the performance and weight of bike components. Katsanis shares examples of 3D printed handlebars and frames that are lighter than their carbon fiber counterparts. He also discusses the future of mass customization in bike design and the impact of regulations on innovation. Finally, he speculates on what bikes may look like in the future if design restrictions were lifted. Chapters 00:00 Podcast intro 06:40 Introduction and Background 11:10 UCI Regulations and Bike Design 17:48 Evolution of Bike Design and UCI Regulations 25:27 Influence of Weight and Aerodynamics on Bike Performance 32:01 Pushing the Limits of Aerodynamics 37:16 Yaw Sensitivity and Aerofoil Sections 40:53 Continual Improvement in Bike Design 42:25 The Evolution of Bike Design 42:51 Aerodynamics and System Drag Reduction 44:21 Track vs. Road Bike Design 47:05 Interactions Between Bike and Rider 48:02 The Importance of Aero in Solo Breakaways 53:00 Weight vs. Aero Debate 56:00 The Impact of Weight on Performance 58:04 The Role of Stiffness in Bike Design 01:04:01 Stiffness and Comfort in Bike Frames 01:11:56 Materials in Bike Design: Steel, Aluminum, Titanium, and Carbon Fiber 01:18:08 The Potential of 3D Printing and Additive Manufacturing 01:19:45 The Limitations of Carbon Fiber 01:21:41 The Potential of 3D Printing 01:24:10 The Surprising Lightness of 3D Printed Titanium 01:28:02 The Future of Mass Customization 01:34:06 The Impact of Regulations on Bike Design 01:43:09 Speculating on the Bike of the Future Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s1-e5-dimitris-katsanis-designing-the-world-s-fastest-bikes/ | |||
| S1, EP4 - Academia or Industry? PhD or no PhD? Career advice | 21 May 2024 | 00:42:32 | |
Summary Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s1-e4-academia-or-industry-phd-or-no-phd-career-advice/ | |||
| S1, EP3 - Prof Tony Purnell - F1, British Cycling, PI Research and much more | 14 May 2024 | 02:15:28 | |
Professor Tony Purnell discusses his journey from a passion for motor racing and engineering in his youth to founding and leading Pi Research, a company specializing in race car electronics. He shares his experiences at university, including a Kennedy Scholarship to MIT, and his early career in the motor racing industry. Tony also explains how Pi Research expanded into the automotive industry and eventually caught the attention of Ford, leading to the company's acquisition. His story highlights the importance of passion, perseverance, and seizing opportunities in pursuing a successful career. Tony shares his experiences in the world of Formula One, from Ford's interest in buying the team to his role in restructuring the Aero department at Jaguar. He discusses the challenges he faced and the politics and dishonesty he encountered in the industry. Tony also reflects on the stress and burnout he experienced and the difficulties he had working with Red Bull. Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s1-e3-prof-tony-purnell-formula-1-british-cycling-and-pi-research/ | |||
| S1, EP2 - Dr Florian Menter - Turbulence Modelling Pioneer | 07 May 2024 | 01:20:54 | |
Florian Menter discusses his journey in the field of computational fluid dynamics (CFD) and the development of the K-Omega SST model. He shares his experiences working at NASA Ames and the collaborative environment in the CFD community. Florian also talks about his decision to return to Germany and his role in the early days of what would be become ANSYS. Florian Menter discusses the birth and development of the SST turbulence model, the challenges of transition modeling, and the future of RANS models. He also explores the potential of machine learning in CFD and shares advice for young researchers. The conversation highlights the importance of pursuing valuable ideas, keeping things simple, and envisioning the outcome of one's work. Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s1-e2-dr-florian-menter-turbulence-modelling-pioneer/ | |||
| S1, EP1 - Neil Ashton - Podcast Intro | 07 May 2024 | 00:09:42 | |
In this short first episode Neil will explain why he's created the podcast, the guests he'll be interviewing and the topics that will be covered. Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s1-e1-podcast-introduction/ | |||
| S4 EP1 - Are AI Agents and Foundation Models About to Rewrite CAE? | 01 Jun 2026 | 00:28:18 | |
In this episode, Neil explores how agents, foundation models, and AI are set to transform the Computer-Aided Engineering (CAE) and Electronic Design Automation (EDA) landscapes. He shares a comprehensive historical perspective and predicts a near-future where AI-driven automation redefines engineering workflows, productivity, and innovation. Main Topics: The evolution of simulation codes from the 1960s to modern commercial software The rise of cloud computing, GPUs, and their impact on CAE and EDA industries The integration of AI, surrogate modeling, and foundation models into simulation workflows The emergence of agentic AI systems capable of autonomously performing complex engineering tasks The strategic responses of major software companies to AI and agent technologies The potential democratization and automation of engineering design through AI agents Critical questions on model ownership, transparency, and industry adoption Timestamps: 00:00 - Podcast intro 00:40 - Introduction: How agents and foundation models will disrupt CAE & EDA 01:40 - Historical overview: From code writing in the 60s to commercial software 03:10 - Growth of aerospace and automotive industry codes and commercialization 04:40 - The impact of HPC, cloud computing, and hardware evolution 06:25 - Rise of cloud SaaS models and "sassification" of simulation tools 07:40 - Big tech entrance: AWS, Microsoft, and Google in CAE & EDA 09:00 - GPU acceleration: Changed landscape in past three to four years 09:10 - The role of AI startups offering surrogate models and real-time simulation 10:40 - Industry consolidation: Mergers and acquisitions among software giants 11:40 - The emergence of foundation models and surrogate systems in simulation 13:00 - The significance of agents: Combining AI, models, and automation 14:10 - Capabilities of autonomous AI agents in complex engineering workflows 15:25 - Practical use cases: Running simulations, setting up experiments, and data analysis 16:10 - Questions about model ownership, open-source codes, and licensing 16:40 - How agent-driven automation could democratize engineering expertise 19:40 - The future of AI in engineering: Collaboration, transparency, and scientific rigor 21:25 - Final thoughts: Opportunities, challenges, and the transformative potential of AI Please note that this episode expresses my personal opinion and does not represent the views of NVIDIA. Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s4-e1-are-ai-agents-and-foundation-models-about-to-rewrite-cae/ | |||
| S3 EP9 - Fluid Intelligence with Johannes Brandstetter and Siddhartha Mishra | 02 Dec 2025 | 01:24:54 | |
In this conversation, Neil Ashton and Prof. Siddhartha Mishra, and Prof. Johannes Brandstetter discuss their recent paper on AI foundation models in computational fluid dynamics (CFD). They explore the backgrounds of the speakers, the journey to writing the paper, the role of AI in CFD, and the challenges of scaling laws and data generation. The discussion also covers model training costs, open questions, and future directions for research in this field. Fluid Intelligence: A Forward Look on AI Foundation Models in Computational Fluid Dynamics : https://arxiv.org/abs/2511.20455v1 Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s3-e9-fluid-intelligence-with-johannes-brandstetter-and-siddhartha-mishra/ | |||
| S3 EP8 - The Conference Connection (HPC, CAE, ML & Engineering) | 01 Nov 2025 | 00:22:37 | |
In this episode, Neil Ashton discusses various conferences and workshops in the automotive, aerospace, and machine learning fields. He highlights the importance of these events for networking, education, and staying updated with industry trends. From the SAE and AIAA events to machine learning workshops, Neil provides insights into what attendees can expect and the value of participating in these gatherings. Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s3-e8-the-conference-connection-hpc-cae-ml-and-engineering/ | |||
| S3 EP7 - 5 key trends for CFD revisited | 15 Oct 2025 | 00:20:08 | |
In this episode of the Neil Ashton podcast, the host revisits key trends in Computational Fluid Dynamics (CFD) from the past year, focusing on the rise of GPUs, advancements in AI and machine learning, the shift to cloud computing, the increasing adoption of high fidelity methods, and ongoing mergers and acquisitions in the industry. Each trend is explored in depth, highlighting the implications for the future of engineering and technology Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s3-e7-five-key-trends-for-cfd-revisited/ | |||
| S3 EP6 Prof. Brian Launder - CFD and Turbulence Modelling Pioneer | 30 Sep 2025 | 01:28:23 | |
Professor Brian Launder, Professor at the University of Manchester and Fellow of the Royal Society and the Royal Academy of Engineering, reflects on more than 50 years of turbulence-modeling and CFD research. Neil and Brian discuss the influence of Professor Brian Spalding, the development of the k-epsilon and second-moment closure models, key collaborators and former students, and advice for early-career researchers. Chapters 00:00 Podcast intro 00:30 Introduction 05:00 Early Academic Journey 10:06 Transition to MIT and Research Focus 16:21 Return to Imperial College and Early Career 21:06 Research Projects and PhD Students 27:46 Development of the k-epsilon model 33:18 CHAM and Career Changes 36:24 Move to UC Davis and New Research Directions 44:05 Challenges and Opportunities in Research 47:07 The Interview Experience 51:14 Transition to Manchester University 52:23 Research Innovations in Turbulence Modeling 57:45 The Development of the TCL Model 01:03:15 Nonlinear Eddy Viscosity Models 01:05:58 Advanced Wall Functions and Their Applications 01:10:09 Reflections on Career and Contributions 01:15:49 Legacy and Impact on Turbulence Modeling Top Turbulence Modelling contributions (https://scholar.google.com/citations?user=Y3JbAK8AAAAJ&hl=en) Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s3-e6-prof-brian-launder-cfd-and-turbulence-modelling-pioneer/ | |||
| S3 EP5 - Joris Poort - CEO and Founder of Rescale | 17 Sep 2025 | 01:39:48 | |
In this episode, Joris Poort, CEO and founder of Rescale, shares his personal journey on founding Rescale as well as his thoughts on the future of CAE. He discusses the challenges of introducing HPC to the cloud market, the traits that make successful founders, and the importance of perseverance and execution in entrepreneurship. Joris reflects on the early days of Rescale, the significance of early investors, and the evolving landscape of cloud computing and AI integration in engineering. The conversation highlights the complexities of transitioning to cloud solutions and the future potential of HPC in various industries. In this conversation, Joris discusses the transformative impact of AI on engineering, particularly in the context of inference, simulation, and automation. He emphasizes the importance of efficiency in engineering processes and how AI can significantly reduce the time required for complex simulations. The discussion also touches on the cultural shifts within organizations as they adapt to AI technologies, the potential for AI surrogates to revolutionize engineering practices, and the challenges of closing the sim-to-real gap. Joris offers insights for aspiring founders, encouraging them to pursue meaningful work that can drive innovation and societal progress. Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s3-e5-joris-poort-ceo-and-founder-of-rescale/ | |||
| S4 EP6 - Daniel Mira on Hydrogen Combustion Modelling and Future Propulsion | 11 Aug 2026 | 01:24:52 | |
Hydrogen combustion, high-fidelity CFD and the future of aircraft propulsion are the focus of this conversation with Dr. Daniel Mira, Head of the Propulsion Technologies Group at the Barcelona Supercomputing Center. Neil and Dani discuss why reacting flows are so difficult to simulate, how hydrogen changes combustion and aircraft design, the limits of RANS, LES and DNS, GPU-native solvers, coding agents and AI surrogate models. Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s4-e6-daniel-mira-on-hydrogen-combustion-modelling-and-future-propulsion/ Topics Why reacting flows are so computationally difficult Hydrogen versus hydrocarbon combustion When hydrogen could reach commercial aviation How engines and aircraft must be redesigned Industrial trust in high-fidelity combustion CFD RANS, LES and DNS for reacting flows Chemistry, load balancing and computational cost Wall modelling in combustion LES GPU acceleration and solver redesign Coding agents for scientific software AI surrogate models and digital engineering workflows Selected resources Daniel Mira and the Propulsion Technologies Group https://ptg.bsc.es/?p=44 Propulsion Technologies Group — research lines https://ptg.bsc.es/research-lines/ BSC — Combustion research https://www.bsc.es/research-development/research-areas/engineering-simulations/combustion Center of Excellence in Combustion (CoEC) https://coec-project.eu/ High-fidelity simulations of the mixing and combustion of a technically premixed hydrogen flame https://upcommons.upc.edu/entities/publication/08a27c10-cb13-4357-a3ab-8e9ec1d706cc Chapters 00:00 Podcast intro 00:39 Introducing Daniel Mira 03:00 Conversation begins 04:55 Why combustion CFD is so hard 10:23 Daniel’s path into hydrogen and jet-engine combustion 12:48 Hydrogen versus hydrocarbon combustion 17:58 Industrial adoption of hydrogen 20:54 Gas turbines, aviation and fuel infrastructure 25:35 How jet engines must change 30:43 Redesigning the whole aircraft 34:46 What will trigger commercial adoption? 37:27 Why aerospace projects take a decade 42:14 RANS, LES and DNS for reacting flows 44:31 Replacing expensive tests with high-fidelity CFD 46:01 The biggest accuracy gaps in combustion LES 49:26 Where the computational cost goes 52:06 Chemistry, species and source-term bottlenecks 55:35 Wall modelling in combustion LES 59:49 GPUs, algorithms and solver redesign 01:08:52 Can coding agents accelerate combustion CFD? 01:12:27 AI surrogate models for combustion 01:24:20 Closing thoughts | |||