KO-PDE-IDENT combines model-X knockoff filters with SHAP-based statistics, recursive feature elimination, and multi-criteria decision making to recover exact PDE structures from noisy data with FDR control.
Controlling the false discovery rate: a practical and powerful approach to multiple testing
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Data-driven sparse identification of governing PDEs via knockoff filters and multi-criteria trade-offs
KO-PDE-IDENT combines model-X knockoff filters with SHAP-based statistics, recursive feature elimination, and multi-criteria decision making to recover exact PDE structures from noisy data with FDR control.