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
