Congratulations to Mohammad for his ICML paper developing a novel, sensitivity-based approach for generative topology optimization 👍 arxiv.org/abs/2606.02179 Our work investigates a fundamental question: What determines whether data-driven topology optimization models generalize?

Congratulations to Bernhard for his SIGGRAPH 2026 Paper 👍 PDF ge.in.tum.de/download/ST-... , Video youtu.be/-1Txagqj4N0 Our key idea: treat particles as samples in four-dimensional space-time. ST-FLIP acts as a temporal anti-aliasing mechanism for FLIP-style solvers.

Congratulations to Hao for his ICML Oral on rotationally equivariant transformers: tum-pbs.github.io/revit-web/ 👍 The key idea is to transform physical fields into local canonical coordinate systems, enabling standard self-attention while preserving physical symmetries

I'm happy to introduce "CRAFT": a new federated learning optimizer that treats aggregation as a geometric correction problem instead of naive averaging. This is great work by Ziqi (FAU) and Qiang (TUM), building on our previous ConIFG optimizer: github.com/tum-pbs/CRAFT

What if pretraining scientific foundation models didn’t require massive datasets at all? We show that this is not only possible, but has a range of neat benefits: our "Tadpole" models learn from canonical PDE data that is generated on-the-fly arxiv.org/abs/2605.15284

For those who already checked out our AeroTransformer last week: please also try Yunjia's live "WebWing" demo at webwing.pbs.cit.tum.de ✈️

I'm very excited to highlight our recent work on how useful NNs are for stability and resolvent analysis of non-linear systems. This is a very fundamental, and classic topic, and Chengyun established a firm connection between the theoretical basis and modern AI methods. github.com/tum-pbs/Nonl...

I'm excited to share our latest work: AeroTransformer — a step toward bringing the foundation model paradigm to real-world aerodynamic design. Code & models: github.com/tum-pbs/Aero... Paper: arxiv.org/abs/2604.18062

Ever wondered if a Transformer model could successively refine a PDE solution, one scale at a time? 🤔 Mario's work shows a way forward: a single model auto-regressively infers and refines flow solutions over finer and finer sets of sample points: arxiv.org/pdf/2604.11403 , github.com/tum-pbs/SAR

I'm excited to highlight the code release of our scalable & efficient PDE Transformer (P3D) at akanota.github.io/p3d/ Please try out the pretrained models, and let us know how it works! Highlights are, e.g., stable inference of 1024^3 rollouts on a single GPU with 90GB 😁

We're happy to report that our Physics-based Flow Matching framework got an accept for ICLR'26! Physics-Based Flow Matching (PBFM) is a principled framework that explicitly targets Pareto-optimal solutions between physics-constraints and data-driven objectives.

Fast rotational equivariance for physics GNNs — the source code is now available: github.com/tum-pbs/stra... Please also check out the full Physics-of-Fluids paper here: pubs.aip.org/aip/pof/arti...

We're very excited to report that our P3D Transformer was accepted at ICLR openreview.net/forum?id=8Ud... We introduce a scalable hybrid CNN–Transformer architecture that pushes neural surrogate modeling into the regime of truly high-resolution 3D simulations.

I'm very happy to report that our autoregressive predictions with generative diffusion models is _finally_ accepted 😁 Congratulations Georg! It's been a long journey, this paper was first submitted to NeurIPS'23, and now, almost 3y later, finally got accepted www.sciencedirect.com/science/arti...

Benchmarking Autoregressive Conditional Diffusion Models for Turbulent Flow Simulation

Simulating turbulent flows is crucial for a wide range of applications, and machine learning-based solvers are gaining increasing relevance. However, …

sciencedirect.com

Great to see our paper on physics-constrained reconstruction / super-res with generative models posted online now at doi.org/10.1063/5.03... 😁 - PDE Transformer as backbone architecture - differentiable physics constraints to guide - and ConFIG as optimizer to resolve conflicts in the gradients

Guiding diffusion models to reconstruct flow fields from sparse data

The reconstruction of unsteady flow fields from limited measurements is a challenging and crucial task for many engineering applications. Machine learning model

doi.org

The SuperWing dataset is a large-scale, open dataset of transonic swept-wing aerodynamics, combining thousands of richly parameterized 3D wing geometries with high-fidelity RANS simulations across the operational flight envelope: arxiv.org/abs/2512.14397

Please join our mini symposium on "AI for Computational Fluid Dynamics - Opportunities and Challenges" MS279 , wccm-eccomas2026.org/event/area/8... at WCCM ECCOMAS in Munich next year in July (July 2026, wccm-eccomas2026.org). Inspiring discussions, and a proper "Mass" at the beergarden 🍻😁

Our full course "advanced deep learning for physics" (ADL4P) is online now at tum-pbs.github.io/ADL4P/ 😁 The course covers AI and neural network techniques for physics simulations & combinations with numerical methods. All recordings, slides and exercises are freely available!

Can AI surrogates outperform their training data? Turns out the answer is yes - with a few caveats 😉 tum-pbs.github.io/emulator-sup... #neurips This surprising behavior leads to interesting and fundamental questions about the role of training data, and about how NN surrogates should be evaluated.

Can your AI surpass the simulator that taught it? What if the key to more accurate PDE modeling lies in questioning your training data's origins? 🤔 Excited to share my #NeurIPS 2025 paper with @thuereygroup.bsky.social: "Neural Emulator Superiority"!

Congratulations to Hao, Aleksandra and Bjoern for their NeurIPS paper tum-pbs.github.io/inc-paper/ 👍 It analyzes how hybrid PDE solvers fundamentally and provably benefit from "indirect" (force-based) corrections rather than direct ones. Baking the corrections via INC reduces error growth!

I wanted to highlight that source code and data for our physics-based flow matching (PBFM) algorithm are online now at: github.com/tum-pbs/PBFM/ feel free to give it a try, and we'd be curious to hear how it works for you!

I'm happy to report that our collaborative project on 3D sparse-reconstruction and super-resolution with diffusion models, physics constraints and PDE Transformers is online now as preprint arxiv.org/abs/2510.19971 and source code github.com/tum-pbs/spar.... Great work Marc, Luis, Qiang and Luca 👍

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

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 😅

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!

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...

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!

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

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