6 papers
CRAFT: Conflict-Resolved Aggregation for Federated Training
Ziqi Wang, Qiang Liu, Nils Thuerey
The aggregation of conflicting client updates remains a fundamental bottleneck in federated learning (FL) under heterogeneous data distributions. Naive averaging can produce a glob…
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…
Neural Emulator Superiority: When Machine Learning for PDEs Surpasses its Training Data
Felix Koehler, Nils Thuerey
Neural operators or emulators for PDEs trained on data from numerical solvers are conventionally assumed to be limited by their training data's fidelity. We challenge this assumpti…
PRDP: Progressively Refined Differentiable Physics
Kanishk Bhatia, Felix Koehler, Nils Thuerey
The physics solvers employed for neural network training are primarily iterative, and hence, differentiating through them introduces a severe computational burden as iterations gro…
Light Transport-aware Diffusion Posterior Sampling for Single-View Reconstruction of 3D Volumes
Ludwic Leonard, Nils Thuerey, Ruediger Westermann
We introduce a single-view reconstruction technique of volumetric fields in which multiple light scattering effects are omnipresent, such as in clouds. We model the unknown distrib…
APEBench: A Benchmark for Autoregressive Neural Emulators of PDEs
Felix Koehler, Simon Niedermayr, Rüdiger Westermann +1
We introduce the Autoregressive PDE Emulator Benchmark (APEBench), a comprehensive benchmark suite to evaluate autoregressive neural emulators for solving partial differential equa…