arXiv Machine Learning October 9, 2026 By Ashraful Islam, Shuichi Nishino, Tomohiro Shiraishi, Ichiro Takeuchi

PSI-SINDy: Post-Selection Inference for Sparse Identification of Nonlinear Dynamics

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PSI‑SINDy introduces a statistical inference framework that quantifies the reliability of dynamical terms selected by the Sparse Identification of Nonlinear Dynamics (SINDy) method. It addresses selection bias arising from using the same noisy trajectory for both selection and inference by employing data thinning to create four independent views for selection and inference. The approach accounts for measurement error and shared noise, and its theoretical validity is established under stated conditions with numerical experiments on simulated and experimental data.

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