Tesfay Zemuy Gebrekidan

Canonical Minimal Polynomial Invariants from Noisy Data via Adaptive Sparse Nullspace Recovery and Gröbner Basis

Discovering conservation laws and algebraic invariants from data is a fundamental challenge in scientific machine learning. Existing methods, such as SINDy-null (KRONIC), successfully recover a single invariant but fail when multiple invariants coexist, returning arbitrary linear...