Summary of Apebench: a Benchmark For Autoregressive Neural Emulators Of Pdes, by Felix Koehler et al.
APEBench: A Benchmark for Autoregressive Neural Emulators of PDEs
by Felix Koehler, Simon Niedermayr, Rüdiger Westermann, Nils Thuerey
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The Autoregressive PDE Emulator Benchmark (APEBench) is a comprehensive evaluation tool for autoregressive neural networks that solve partial differential equations. APEBench is built on JAX and features a differentiable simulation framework that uses efficient pseudo-spectral methods to solve 46 distinct PDEs across 1D, 2D, and 3D spaces. The benchmark provides a novel taxonomy for unrolled training and a unique identifier for PDE dynamics that relates to the stability criteria of classical numerical methods. APEBench enables the evaluation of various neural architectures and supports differentiable physics training and neural-hybrid emulators. The benchmark also emphasizes rollout metrics to understand temporal generalization, providing insights into the long-term behavior of emulating PDE dynamics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Autoregressive PDE Emulator Benchmark (APEBench) is a new way to test how well computer models can solve math problems that describe how things change over time. APEBench uses a special tool called JAX and has a built-in simulator that can solve many different types of math problems. This makes it easier to compare how well different computer models do at solving these problems. The benchmark also helps us understand how well the models work in the long run by looking at how they behave over time. |
Keywords
» Artificial intelligence » Autoregressive » Generalization
