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New framework PSI-SINDy enhances reliability of identifying nonlinear dynamics

By PulseAugur Editorial · [1 sources] ·

Researchers have developed PSI-SINDy, a new statistical inference framework designed to quantify the reliability of dynamical terms identified by the Sparse Identification of Nonlinear Dynamics (SINDy) method. This approach addresses the challenge of selection bias that arises when using the same noisy data for both term selection and significance assessment. PSI-SINDy employs data thinning to create independent data views, enabling accurate inference for selected dynamical terms while accounting for measurement errors and shared noise. AI

IMPACT Enhances the statistical rigor of data-driven methods used in scientific discovery and modeling.

RANK_REASON The item describes a new method and theoretical framework for a specific scientific problem, published on arXiv. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework PSI-SINDy enhances reliability of identifying nonlinear dynamics

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The item describes a new method and theoretical framework for a specific scientific problem, published on arXiv. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Ashraful Islam, Shuichi Nishino, Tomohiro Shiraishi, Ichiro Takeuchi ·

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

    arXiv:2610.11486v1 Announce Type: cross Abstract: 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 th…

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