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Automated model discovery of partial differential equations (PDEs) usually considers a single experiment or dataset to infer the underlying governing equations.
Model selection and estimation in regression with grouped variables
Ming Yuan and Yi Lin · 2006
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Consistent group selection in high-dimensional linear regression
Fengrong Wei and Jian Huang · 2010
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Stability selection
Nicolai Meinshausen and Peter Bühlmann · 2010
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Discovering governing equations from data by sparse identification of nonlinear dynamical systems
Steven L Brunton, Joshua L Proctor, and J Nathan Kutz · 2016
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Data-driven discovery of partial differential equations
Samuel H Rudy, Steven L Brunton, Joshua L Proctor, and J Nathan Kutz · 2017
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Learning partial differential equations via data discovery and sparse optimization
Hayden Schaeffer · 2017
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Suryanarayana Maddu, Bevan L. Cheeseman, Ivo F. Sbalzarini, and Christian L. Müller · 2019
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Data-driven identification of parametric partial differential equations
Samuel Rudy, Alessandro Alla, Steven L Brunton, and J Nathan Kutz · 2019
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Discovery of physics from data: Universal laws and discrepancies
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Learning physically consistent mathematical models from data using group sparsity
Suryanarayana Maddu, Bevan L. Cheeseman, Christian L. Müller, and Ivo F. Sbalzarini · 2020
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Implicit neural representations with periodic activation functions
Vincent Sitzmann, Julien Martel, Alexander Bergman, David Lindell, and Gordon Wetzstein · 2020
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Deepmod: Deep learning for model discovery in noisy data
Gert-Jan Both, Subham Choudhury, Pierre Sens, and Remy Kusters · 2021
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Georges Tod, Gert-Jan Both, and Remy Kusters · 2021
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