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Keyword: physics-constrained neural networks ×Clear all

2 claims from 1 paper

1. Earth and Planetary Sciences › Oceanographic and Atmospheric Processes

   Benchmarking of Machine Learning Ocean Subgrid Parameterizations in an Idealized Model

   Ross, Li, Perezhogin, Fernandez‐Granda and Zanna · Journal of Advances in Modeling Earth Systems · 2022

   The paper benchmarks machine learning ocean subgrid parameterizations online, on new domains and across dataset design choices, and proposes an equation-discovery approach that generalizes better.

   Unchecked2 claims
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   1. UncheckedIn this benchmark, how high-resolution ocean data are filtered and coarse-grained matters a great deal, and the choice should suit the intended application.“We find that the choice of filtering and coarse‐graining operator is particularly critical and this choice should be guided by the application.”
   2. Unchecked“We also show that all of our physics‐constrained NNs are stable and perform well when implemented online, but generalize poorly to new regimes.”