Ill-Conditioning in Dictionary-Based Dynamic-Equation Learning: A Systems Biology Case Study
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. In such cases, measurement noise may lead to widely different recovered models, obscuring the true underlying dynamics and hindering accurate system identification. Although spar...
| Authors | Yuxiang Feng, Niall M Mangan, Manu Jayadharan |
|---|---|
| Primary category | q-bio.QM |
| Published | 11 March 2026 |
| Last updated at source | 11 March 2026 |
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