PDEBENCH: An Extensive Benchmark for Scientific Machine Learning

TL;DR

PDEBench is a comprehensive benchmark suite for Scientific Machine Learning involving PDEs, providing diverse, extensive datasets, standardized APIs, and new evaluation metrics to facilitate fair comparison and advancement of ML models in physical simulations.

Contribution

The paper introduces PDEBench, a large, extensible benchmark suite with diverse PDE problems, extensive datasets, and new evaluation metrics for Scientific ML research.

Findings

PDEBench covers a wider range of PDEs than existing benchmarks.

It provides larger, more comprehensive datasets for model training and evaluation.

New metrics help identify challenging tasks for current ML methods.

Abstract

Machine learning-based modeling of physical systems has experienced increased interest in recent years. Despite some impressive progress, there is still a lack of benchmarks for Scientific ML that are easy to use but still challenging and representative of a wide range of problems. We introduce PDEBench, a benchmark suite of time-dependent simulation tasks based on Partial Differential Equations (PDEs). PDEBench comprises both code and data to benchmark the performance of novel machine learning models against both classical numerical simulations and machine learning baselines. Our proposed set of benchmark problems contribute the following unique features: (1) A much wider range of PDEs compared to existing benchmarks, ranging from relatively common examples to more realistic and difficult problems; (2) much larger ready-to-use datasets compared to prior work, comprising multiple simulation runs across a larger number of initial and boundary conditions and PDE parameters; (3) more extensible source codes with user-friendly APIs for data generation and baseline results with popular machine learning models (FNO, U-Net, PINN, Gradient-Based Inverse Method). PDEBench allows researchers to extend the benchmark freely for their own purposes using a standardized API and to compare the performance of new models to existing baseline methods. We also propose new evaluation metrics with the aim to provide a more holistic understanding of learning methods in the context of Scientific ML. With those metrics we identify tasks which are challenging for recent ML methods and propose these tasks as future challenges for the community. The code is available at https://github.com/pdebench/PDEBench.