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Found 3 papers, 2 papers with code
Comparison of neural closure models for discretised PDEs
Of the two trajectory fitting procedures, the discretise-then-optimise approach produces more accurate models than the optimise-then-discretise approach.
Stochastic parameterization with VARX processes
We show through various statistical criteria that the proposed VARX performs very well for the unimodal configuration, while keeping the number of parameters linear in the number of model variables.
Resampling with neural networks for stochastic parameterization in multiscale systems
This method is used to formulate a parameterization that is stochastic, taking the uncertainty of the unresolved scales into account.
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