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N. Thuerey's research group at TUM @thuereygroup.bsky.social · 22d

I'm very excited to introduce P3D: our PDE-Transformer architecture in 3 dimensions by . Demonstrated for unprecedented 512^3 resolutions! That means the Transformer produces over 400 million degrees of freedom in one go 😀 a regime that was previously out of reach: arxiv.org/abs/2509.10186

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N. Thuerey's research group at TUM @thuereygroup.bsky.social · 29d

Congratulations to Bjoern for his accepted PoF paper on equivariant GraphNets 👍 doi.org/10.1063/5.02...
the core idea is a very generic and powerful one: we compute a local Eigenbasis from flow features for equivariance. Mathematically it's identical to previous approaches, but faster and simpler 😅

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N. Thuerey's research group at TUM @thuereygroup.bsky.social · Aug 1

I also wanted to mention that our paper detailing the differentiable SPH solver by Rene is online now on arxiv: arxiv.org/abs/2507.21684 If you're interested in fast and efficient neighborhoods, differentiable SPH operators and neat first optimization and learning tasks, please take a look!

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N. Thuerey's research group at TUM @thuereygroup.bsky.social · Jun 30

Get ready for the PDE-Transformer: our new NN architecture tailored to scientific tasks 😁 It combines hierarchical processing (UDiT), scalability (SWin) and flexible conditioning mechanisms. Code and paper available at tum-pbs.github.io/pde-transfor...

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N. Thuerey's research group at TUM @thuereygroup.bsky.social · Jun 17

I'm really excited to share our latest work combining physics priors with probabilistic models: Flow Matching Meets PDEs - A Unified Framework for Physics-Constrained Generation , arxiv.org/abs/2506.08604 , great work by Giacomo and Qiang!

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N. Thuerey's research group at TUM @thuereygroup.bsky.social · Jun 13

Have you faced challenges like SPH-based inverse problems, or learning Lagrangian closure models?
For these we’re excited to announce the first public release of DiffSPH , our differentiable Smoothed Particle Hydrodynamics solver.
Code: diffsph.fluids.dev
Short demo: lnkd.in/dYABSeKG

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N. Thuerey's research group at TUM @thuereygroup.bsky.social · Jun 4

Congratulations to Bernhard for his first #SIGGRAPH paper! Great work 👍 His two-phase Navier-Stokes solver is even more impressive given the fact that it's all done on a regular workstation, and without a GPU. Enjoy the sims in full screen & hi-quality here: youtu.be/nt9BohngvoE

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N. Thuerey's research group at TUM @thuereygroup.bsky.social · Jun 2

I also just recorded a quick overview video for our new PICT solver: youtu.be/GGLidL0oT3s , enjoy! In case you missed it: PICT provides a new fully-differentiable multi-block Navier-Stokes solver for AI and learning tasks in PyTorch, e.g. learning turbulence closure in 3D

Introducing PICT: the differentiable Fluid Solver for AI & machine learning in PyTorch

YouTube video by Nils Thuerey

youtu.be

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N. Thuerey's research group at TUM @thuereygroup.bsky.social · May 28

I'd like to highlight PICT, our new differentiable Fluid Solver built for AI & learning: github.com/tum-pbs/PICT

Simulating fluids is hard, and learning 3D closure models even harder: This is where PICT comes in — a GPU-accelerated, fully differentiable fluid solver for PyTorch 🥳

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N. Thuerey's research group at TUM @thuereygroup.bsky.social · May 21

I can highly recommend checking out Mario's talk about our Diffusion Graph Net paper from ICLR'25: www.youtube.com/watch?v=4Vx_... , enjoy!

Learning Distributions of Complex Fluid Simulations with Diffusion Graph Networks

YouTube video by Mario Lino

www.youtube.com

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N. Thuerey's research group at TUM @thuereygroup.bsky.social · May 13

I wanted to highlight PBDL's brand-new sections on diffusion models with code and derivations! Great work by Benjamin Holzschuh, with neat Jupyter notebooks 👍 All the way from normalizing flow basics over score matching to denoising & flow matching. E.g., colab.research.google.com/github/tum-p...

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N. Thuerey's research group at TUM @thuereygroup.bsky.social · Apr 23

If you're at #ICLR 2025 in Singapore, please check out our posters 🤗 I'm sure it's going to be a great conference! Have fun everyone...

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N. Thuerey's research group at TUM @thuereygroup.bsky.social · Apr 11

I wanted to highlight that our project website (with code!) for our progressively-refined training with physics simulations is up now at: kanishkbh.github.io/prdp-paper/ #ICLR25 , the main ideas are: match network approximation and physics accuracy, refine the physics over the course of training.

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N. Thuerey's research group at TUM @thuereygroup.bsky.social · Mar 28

The full PBDL book is available in a single PDF now arxiv.org/pdf/2109.05237, and has grown to 451 pages 😳 Enjoy all the new highlights on generative models, simulation-based constraints and long term stability with diffusion models 😁

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N. Thuerey's research group at TUM @thuereygroup.bsky.social · Mar 20

I'm very excited to highlight PBDL v0.3 www.physicsbaseddeeplearning.org, the latest version of our physics-based deep learning "book" 🥳 This version features a huge new chapter on generative AI, covering topics ranging from the derivation, over graph-based inference to physics-based constraints!

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N. Thuerey's research group at TUM @thuereygroup.bsky.social · Feb 25

Congratulations to Kanishk and Felix 👍 for their #ICLR'25 paper "Progressively Refined Differentiable Physics" kanishkbh.github.io/prdp-paper/ , the key insight is that training can be accelerated substantially by using fast approximates of the gradient (especially in early phases of training)

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N. Thuerey's research group at TUM @thuereygroup.bsky.social · Feb 19

Congratulations to Youssef and Benjamin 👍 for their #ICLR'25 paper on Truncated Diffusion Sampling openreview.net/forum?id=0Fb... It investigates several key questions of generative AI and diffusion for physics simulations to improve accuracy via Tweedie's formula

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Reposted by N. Thuerey's research group at TUM

Munich Center for Machine Learning @munichcenterml.bsky.social · Feb 11

Can AI Help Solve Complex Physics Equations? Meet APEBench, an innovative benchmark suite introduced by our Junior Member Felix Köhler, together with our PIs Rüdiger Westermann and Nils Thuerey as well as co-author Simon Niedermayr. Read more: mcml.ai/news/2025-02...

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N. Thuerey's research group at TUM @thuereygroup.bsky.social · Feb 7

Congratulations also to Patrick 👍 for his ICLR paper on Temporal Difference (TD) learning openreview.net/forum?id=j3b... , in it We solve the decades-old puzzle of why TD can solve complex RL tasks that Gradient Descent cannot. Our novel theory shows for 2D how TD can counter ill-conditioning 🤗

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N. Thuerey's research group at TUM @thuereygroup.bsky.social · Feb 3

Congrats to Qiang 👏 for the accept of his #ICLR paper on the "ConFIG" optimizer openreview.net/forum?id=APo... Conflict free learning for PINNs, multi task objectives and more! Outperforms all existing optimizers 😁 source code and examples are online at tum-pbs.github.io/ConFIG/

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N. Thuerey's research group at TUM @thuereygroup.bsky.social · Jan 28

Regarding our 'Diffusion Graph Net' paper at #ICLR'25 openreview.net/forum?id=uKZ..., it's also worth mentioning that the full source code is already online github.com/tum-pbs/dgn4... , complete with notebooks, flow matching, and the full hierarchical diffusion graph net architecture 😁

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N. Thuerey's research group at TUM @thuereygroup.bsky.social · Jan 27

Congrats to Mario 👏 for the accept of his ICLR paper on "Diffusion Graph Nets", it targets predicting complex distributions of flow states on unstructured meshes openreview.net/forum?id=uKZ... It works even if the training data contains only a fraction of the flow statistics per case.

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N. Thuerey's research group at TUM @thuereygroup.bsky.social · Jan 23

I’d like to thank everyone contributing to our five accepted ICLR papers for the hard work! Great job everyone 👍 Here’s a quick list, stay tuned for details & code in the upcoming weeks…

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N. Thuerey's research group at TUM @thuereygroup.bsky.social · Jan 15

Out of curiosity, I recently re-ran the KS equation tests in our "unrolling" paper (github.com/tum-pbs/unro...), and interestingly the effects are even stronger with long training. Up to 10x now for relative errors:

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N. Thuerey's research group at TUM @thuereygroup.bsky.social · Dec 13

Liwei's paper on deep learning-based predictive modeling of airfoil flows is online at PoF now doi.org/10.1063/5.02... 👍 Long-term stability 🐎 and correct transition from mean flow. Linear stability analysis 📉 of the NN Jacobians around the mean confirms the accuracy of the trained operator ⭐️

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