2 citations · 2 across the 5 of their papers we have counts for
5 papers · 1 filter
StocBench: A Benchmark for Generative Modeling of Stochastic Dynamics
Sebastian Pfister, Benjamin Holzschuh, Nils Thuerey
We benchmark transport-based generative models as well as distillation-based few-step methods for the probabilistic forecasting of stochastic fluid flows, with a particular focus o…
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…
Physics vs Distributions: Pareto Optimal Flow Matching with Physics Constraints
Giacomo Baldan, Qiang Liu, Alberto Guardone +1
Physics-constrained generative modeling aims to produce high-dimensional samples that are both physically consistent and distributionally accurate, a task that remains challenging…
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…
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…