%0 Generic %1 otness2021extensible %A Otness, Karl %A Gjoka, Arvi %A Bruna, Joan %A Panozzo, Daniele %A Peherstorfer, Benjamin %A Schneider, Teseo %A Zorin, Denis %D 2021 %K benchmark dataset neuralpde todo:read %T An Extensible Benchmark Suite for Learning to Simulate Physical Systems %U http://arxiv.org/abs/2108.07799 %X Simulating physical systems is a core component of scientific computing, encompassing a wide range of physical domains and applications. Recently, there has been a surge in data-driven methods to complement traditional numerical simulations methods, motivated by the opportunity to reduce computational costs and/or learn new physical models leveraging access to large collections of data. However, the diversity of problem settings and applications has led to a plethora of approaches, each one evaluated on a different setup and with different evaluation metrics. We introduce a set of benchmark problems to take a step towards unified benchmarks and evaluation protocols. We propose four representative physical systems, as well as a collection of both widely used classical time integrators and representative data-driven methods (kernel-based, MLP, CNN, nearest neighbors). Our framework allows evaluating objectively and systematically the stability, accuracy, and computational efficiency of data-driven methods. Additionally, it is configurable to permit adjustments for accommodating other learning tasks and for establishing a foundation for future developments in machine learning for scientific computing.

@misc{otness2021extensible, abstract = {Simulating physical systems is a core component of scientific computing, encompassing a wide range of physical domains and applications. Recently, there has been a surge in data-driven methods to complement traditional numerical simulations methods, motivated by the opportunity to reduce computational costs and/or learn new physical models leveraging access to large collections of data. However, the diversity of problem settings and applications has led to a plethora of approaches, each one evaluated on a different setup and with different evaluation metrics. We introduce a set of benchmark problems to take a step towards unified benchmarks and evaluation protocols. We propose four representative physical systems, as well as a collection of both widely used classical time integrators and representative data-driven methods (kernel-based, MLP, CNN, nearest neighbors). Our framework allows evaluating objectively and systematically the stability, accuracy, and computational efficiency of data-driven methods. Additionally, it is configurable to permit adjustments for accommodating other learning tasks and for establishing a foundation for future developments in machine learning for scientific computing.}, added-at = {2023-12-05T22:15:48.000+0100}, author = {Otness, Karl and Gjoka, Arvi and Bruna, Joan and Panozzo, Daniele and Peherstorfer, Benjamin and Schneider, Teseo and Zorin, Denis}, biburl = {https://www.bibsonomy.org/bibtex/2b6ad5a4e264bcb1c873e6f964f5164f6/annakrause}, description = {An Extensible Benchmark Suite for Learning to Simulate Physical Systems - 2108.07799.pdf}, interhash = {6074cc311b5a8a30383b7bef425c0b34}, intrahash = {b6ad5a4e264bcb1c873e6f964f5164f6}, keywords = {benchmark dataset neuralpde todo:read}, note = {cite arxiv:2108.07799Comment: Accepted to NeurIPS 2021 track on datasets and benchmarks}, timestamp = {2023-12-13T20:15:18.000+0100}, title = {An Extensible Benchmark Suite for Learning to Simulate Physical Systems}, url = {http://arxiv.org/abs/2108.07799}, year = 2021 }