On sparse regression, Lp‐regularization, and automated model discovery

Sparse regression and feature extraction are the cornerstones of knowledge discovery from massive data. Their goal is to discover interpretable and predictive models that provide simple relationships among scientific variables. While the statistical tools for model discovery are well established in...

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Veröffentlicht in:	International journal for numerical methods in engineering Jg. 125; H. 14
Hauptverfasser:	McCulloch, Jeremy A., St. Pierre, Skyler R., Linka, Kevin, Kuhl, Ellen
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Hoboken, USA John Wiley & Sons, Inc 30.07.2024 Wiley Subscription Services, Inc
Schlagworte:	automated model discovery Bias constitutive modeling Feature extraction hyperelasticity Kinematics Lp regularization Neural networks Prediction models Regression models Regularization sparse regression Sparsity Statistical analysis
ISSN:	0029-5981, 1097-0207
Online-Zugang:	Volltext
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