16 citations · 23 across the 8 of their papers we have counts for
9 papers
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…
Shieldstral
Antonia Calvi, Avinash Sooriyarachchi, Giada Pistilli +273
We introduce Shieldstral, a 3B-parameter policy-adaptive multimodal safety classifier that matches or outperforms models nearly 7 its size on text safety benchmarks and set…
Tadpole: Autoencoders as Foundation Models for 3D PDEs with Online Learning
Qiang Liu, Felix Koehler, Benjamin Holzschuh +1
We introduce Tadpole, a novel foundation model for three-dimensional partial differential equations (PDEs) that addresses key challenges in transferability, scalability to high dim…
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…
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…