Ill-Conditioning in Dictionary-Based Dynamic-Equation Learning: A Systems Biology Case Study

ArXi:2603.11330v1 Announce Type: cross Data-driven discovery of governing equations from time-series data provides a powerful framework for understanding complex biological systems. Library-based approaches that use sparse regression over candidate functions have shown considerable promise, but they face a critical challenge when candidate functions become strongly correlated: numerical ill-conditioning. Poor or restricted sampling, together with particular choices of candidate libraries, can produce strong multicollinearity and numerical instability.