3 papers
cs.LG2025
P3D: Scalable Neural Surrogates for High-Resolution 3D Physics Simulations with Global Context
Benjamin Holzschuh, Georg Kohl, Florian Redinger +1
We present a scalable framework for learning deterministic and probabilistic neural surrogates for high-resolution 3D physics simulations. We introduce a hybrid CNN-Transformer bac…
cs.LG2025
PDE-Transformer: Efficient and Versatile Transformers for Physics Simulations
Benjamin Holzschuh, Qiang Liu, Georg Kohl +1
We introduce PDE-Transformer, an improved transformer-based architecture for surrogate modeling of physics simulations on regular grids. We combine recent architectural improvement…
cs.LG2024
Flow Matching for Posterior Inference with Simulator Feedback
Benjamin Holzschuh, Nils Thuerey
Flow-based generative modeling is a powerful tool for solving inverse problems in physical sciences that can be used for sampling and likelihood evaluation with much lower inferenc…