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Found 3 papers, 2 papers with code
Learning physics-based reduced models from data for the Hasegawa-Wakatani equations
We first use the data obtained via a direct numerical simulation of the HW equations starting from a specific initial condition and train OpInf ROMs for predictions beyond the training time horizon.
Context-aware learning of hierarchies of low-fidelity models for multi-fidelity uncertainty quantification
When training low-fidelity models, the proposed approach takes into account the context in which the learned low-fidelity models will be used, namely for variance reduction in Monte Carlo estimation, which allows it to find optimal trade-offs between training and sampling to minimize upper bounds of the mean-squared errors of the estimators for given computational budgets.
Reduced operator inference for nonlinear partial differential equations
First, ideas from projection-based model reduction are used to explicitly parametrize the learned model by low-dimensional polynomial operators which reflect the known form of the governing PDE.
