Sparse regression yields explicit invariant polynomial SGS closures for LES on anisotropic grids that achieve neural-network accuracy with simpler forms and lower computational cost.
Geophysical Research Letters , volume =
4 Pith papers cite this work, alongside 236 external citations. Polarity classification is still indexing.
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A data-driven ABL flux parameterization using convolution operators on mean profiles, trained and tested on LES, improves on standard K-profile closures while remaining interpretable.
AutoSINDy automatically builds a tailored basis library from PySR symbolic regression and applies SINDy to recover ground-truth nonlinear dynamics with 92.8% success under noise.
BG-SINDy reformulates l0-constrained regression as term-level l2,0 regularization and uses progressive pruning guided by balance contributions to recover small-coefficient terms in multiscale PDEs.
citing papers explorer
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Discovery of Sparse Invariant Subgrid-Scale Closures via Dissipation-Controlled Training for Large Eddy Simulation on Anisotropic Grids
Sparse regression yields explicit invariant polynomial SGS closures for LES on anisotropic grids that achieve neural-network accuracy with simpler forms and lower computational cost.
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Data-Driven Flux Parameterization for the Atmospheric Boundary Layer
A data-driven ABL flux parameterization using convolution operators on mean profiles, trained and tested on LES, improves on standard K-profile closures while remaining interpretable.
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Discovery of Nonlinear Dynamics with Automated Basis Function Generation
AutoSINDy automatically builds a tailored basis library from PySR symbolic regression and applies SINDy to recover ground-truth nonlinear dynamics with 92.8% success under noise.
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Balance-Guided Sparse Identification of Multiscale Nonlinear PDEs with Small-coefficient Terms
BG-SINDy reformulates l0-constrained regression as term-level l2,0 regularization and uses progressive pruning guided by balance contributions to recover small-coefficient terms in multiscale PDEs.