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An Extensible Benchmark Suite for Learning to Simulate Physical Systems
Karl Otness · Arvi Gjoka · Joan Bruna · Daniele Panozzo · Benjamin Peherstorfer · Teseo Schneider · Denis Zorin

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in Dataset and Benchmark Poster Session 3 »
Event URL: https://openreview.net/forum?id=pY9MHwmrymR »

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 simulation 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.

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Author Information

Karl Otness (New York University)
Arvi Gjoka (New York University)
Joan Bruna (NYU)
Daniele Panozzo (NYU)
Benjamin Peherstorfer (New York University)
Teseo Schneider
Denis Zorin (New York University)

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