Representative PDE Benchmarks
Introduced by Raonić et al. in Convolutional Neural Operators for robust and accurate learning of PDEs
Given the lack of consensus on a standard setof benchmarks for machine learning of PDEs, we propose a new suite of benchmarks here. Our aims in this regard are to ensure i) sufficient diversity among the types of PDE considered ii) access to training and test data is readily available for rapid prototyping and reproducibility iii) intrinsic computational complexity of problem to make sure that it is worthwhile to design fast surrogates to classical PDE solvers for a particular problem
Papers
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