SWhat changed
Regresses measured state derivatives onto a candidate nonlinear-function library using sparsity, including sequential thresholded least squares, to identify compact dynamical models.
Scientific scope
Regresses measured state derivatives onto a candidate nonlinear-function library using sparsity, including sequential thresholded least squares, to identify compact dynamical models. Sparsity is relative to chosen library/coordinates; adequate measurements, derivative quality and identifiability matter. No recovery of arbitrary governing laws, invention of LASSO or universal noise robustness.
Evidence
The linked original sources describe this contribution and its evidence limits. WRS has not independently reconstructed the full scientific claims. Sparsity is relative to chosen library/coordinates; adequate measurements, derivative quality and identifiability matter. No recovery of arbitrary governing laws, invention of LASSO or universal noise robustness.
Contributors
- Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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