AI RESEARCH
Balance-Guided Sparse Identification of Multiscale Nonlinear PDEs with Small-coefficient Terms
arXiv CS.LG
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ArXi:2604.18414v1 Announce Type: new Data-driven discovery of governing equations has advanced significantly in recent years; however, existing methods often struggle in multiscale systems where dynamically significant terms may have small coefficients. Therefore, we propose Balance-Guided SINDy (BG-SINDy) inspired by the principle of dominant balance, which reformulates $\ell_0$-constrained sparse regression as a term-level $\ell_{2,0}$-regularized problem and solves it using a progressive pruning strategy.