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Automated data-driven modeling, the process of directly discovering the governing equations of a dynamical system from data, ... Machine learning is enabling the discovery of dynamical systems models and governing equations purely from measurement data ... This video discusses how to choose an effective library of candidate terms Speaker: Kadierdan Kaheman Event: Second Symposium on Machine Learning and Dynamical Systems ... In this video, Kadierdan Kaheman describes SINDy-PI: A Constantine Caramanis (University of Texas at Austin) ...

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Sparse Identification of Nonlinear Dynamics (SINDy): Sparse Machine Learning Models 5 Years Later!
Sparse Nonlinear Dynamics Models with SINDy, Part 4: The Library of Candidate Nonlinearities
PySINDy tutorial 2: Choosing algorithm hyperparameters
Sparse Identification of Nonlinear Dynamics (SINDy)
SINDy-PI: A Robust Algorithym for Parallel Implicit Sparse Identification of Nonlinear Dynamics
PySINDy: A Python Library for Model Discovery
PySINDy GUI tutorial
Sparse Nonlinear Dynamics Models with SINDy, Part 2: Training Data & Disambiguating Models
PySINDy tutorial 5: Building in physical priors with constraints
Sparse identification of nonlinear dynamics (SINDy) (DS4DS 6.06)
SINDy-PI: A robust algorithm for parallel implicit sparse identification of nonlinear dynamics
High Dimensional Robust Sparse Regression

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Last Updated: June 7, 2026

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PySINDy tutorial 4: robust sparse system identification by ensembling
PySINDy tutorial 3: robust sparse system identification
PySINDy tutorial 1: overview of PySINDy for sparse system identification
Sparse Identification of Nonlinear Dynamics (SINDy): Sparse Machine Learning Models 5 Years Later!
Sparse Nonlinear Dynamics Models with SINDy, Part 4: The Library of Candidate Nonlinearities
PySINDy tutorial 2: Choosing algorithm hyperparameters
Sparse Identification of Nonlinear Dynamics (SINDy)
SINDy-PI: A Robust Algorithym for Parallel Implicit Sparse Identification of Nonlinear Dynamics