16 citations · 25 across the 8 of their papers we have counts for
11 papers
Cautious Conflict-Free Inverse Gradient Methods
Damian Hattler, Qiang Liu, Nils Thuerey
Partial differential equations (PDEs) are indispensable tools for modeling complex phenomena in physics and engineering. Physics-Informed Neural Networks (PINNs) have emerged as a…
Compute-optimal data scaling for neural surrogates via multi-fidelity training
Paul Setinek, Fabian Paischer, Nils Thuerey +2
Neural surrogates for Partial Differential Equations (PDEs) are usually trained on synthetic data from numerical simulators. As models grow and scaling laws demand larger datasets,…
Geometry Meets Physics: Data-Efficient Pre-Training for Unstructured Neural PDE Solvers
Luis Medrano-Navarro, Giacomo Baldan, Qiang Liu +4
Neural surrogate models for Partial Differential Equations (PDEs) on unstructured 3D geometries are often limited by poor generalization and the high cost of generating large-scale…
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…
Guiding diffusion models to reconstruct flow fields from sparse data
Marc Amorós-Trepat, Luis Medrano-Navarro, Qiang Liu +2
The reconstruction of unsteady flow fields from limited measurements is a challenging and crucial task for many engineering applications. Machine learning models are gaining popula…
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…
Solving Inverse Physics Problems with Score Matching
Benjamin J. Holzschuh, Simona Vegetti, Nils Thuerey
We propose to solve inverse problems involving the temporal evolution of physics systems by leveraging recent advances from diffusion models. Our method moves the system's current…
Realistic galaxy images and improved robustness in machine learning tasks from generative modelling
Benjamin J. Holzschuh, Conor M. O'Riordan, Simona Vegetti +2
We examine the capability of generative models to produce realistic galaxy images. We show that mixing generated data with the original data improves the robustness in downstream m…