7 citations · 9 across the 4 of their papers we have counts for
4 papers
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…
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…
Symmetric Basis Convolutions for Learning Lagrangian Fluid Mechanics
Rene Winchenbach, Nils Thuerey
Learning physical simulations has been an essential and central aspect of many recent research efforts in machine learning, particularly for Navier-Stokes-based fluid mechanics. Cl…