7 citations · 7 across the 5 of their papers we have counts for
6 papers
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,…
From Numerical Simulators of PDEs to Neural Emulators and Back
Felix Koehler
Simulation is central to modern engineering and science, but the cost of numerical solvers for partial differential equations (PDEs) remains a bottleneck whenever fast or many-quer…
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…
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…