PDEBench: An Extensive Benchmark for Scientific Machine LearningDownload PDF

03 Jun 2022, 15:19 (modified: 13 Oct 2022, 18:20)NeurIPS 2022 Datasets and Benchmarks Readers: Everyone
Keywords: Scientific Machine Learning, Benchmark, Partial Differential Equations, PINN, FNO, U-Net, Inverse problem
TL;DR: We provide a benckmark for Scientific Machine Learning
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 repre- sentative 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 con- tribute the following unique features: (1) A much wider range of PDEs compared to existing benchmarks, ranging from relatively common examples to more real- istic and difficult problems; (2) much larger ready-to-use datasets compared to prior work, comprising multiple simulation runs across a larger number of ini- tial 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 pur- poses 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.
Supplementary Material: pdf
URL: https://github.com/pdebench/PDEBench
Dataset Url: Baselines url https://github.com/pdebench/PDEBench Dataset permanent url https://darus.uni-stuttgart.de/dataverse/sciml_benchmark Temporary url PDEBench Dataset https://darus.uni-stuttgart.de/privateurl.xhtml?token=1be27526-348a-40ed-9fd0-c62f588efc01 PDEBench Pre-Trained Models https://darus.uni-stuttgart.de/privateurl.xhtml?token=cd862f8c-8e1b-49d2-b4da-b35f8df5ac85 Permanent url PDEBench Dataset https://darus.uni-stuttgart.de/dataset.xhtml?persistentId=doi:10.18419/darus-2986 PDEBench Pre-Trained Models https://darus.uni-stuttgart.de/dataset.xhtml?persistentId=doi:10.18419/darus-2987 Dataset DOI doi:10.18419/darus-2986 doi:10.18419/darus-2987 DOI url http://dx.doi.org/10.18419/darus-2986 http://dx.doi.org/10.18419/darus-2987
License: MIT for solver code and baseline code NLE Academic License (Academic or non-profit organization noncommercial research use only) for selected code (one solver and baseline) Dataset license CC BY
Author Statement: Yes
Contribution Process Agreement: Yes
In Person Attendance: Yes
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