AI RESEARCH
Data-driven sparse identification of governing PDEs via knockoff filters and multi-criteria trade-offs
arXiv CS.LG
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ArXi:2605.26631v1 Announce Type: cross We propose KO-PDE-IDENT, a data-driven framework for identifying parsimonious partial differential equations (PDEs) with false discovery rate (FDR) control. PDE discovery from noisy observations is often hindered by extreme multicollinearity among candidate terms, which causes typical sparse-regression methods to select spurious terms. To address this problem, KO-PDE-IDENT initially mines a set of potential candidate terms via model-X knockoff filters with finite-sample FDR control, then refines and ranks the surviving PDE alternatives.