PSI-SINDy: Post-Selection Inference for Sparse Identification of Nonlinear Dynamics
Sparse identification of nonlinear dynamics (SINDy) is a data-driven framework for discovering governing dynamics from time-series data by identifying a sparse subset of candidate dynamical terms from a prespecified library. In this work, we develop a statistical inference framework for quantifying the reliability of dynamical terms selected by SINDy through hypothesis tests and confidence intervals. A key difficulty is that using the same noisy trajectory for both selecting dynamical terms and assessing their statistical significance can introduce selection bias. Post-selection inference provides a principled framework for addressing such bias, and we propose PSI-SINDy, a post-selection ...
| Authors | Ashraful Islam, Shuichi Nishino, Tomohiro Shiraishi et al. |
|---|---|
| Primary category | stat.ML |
| Published | 8 October 2026 |
| Last updated at source | 8 October 2026 |
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