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Machine learning (ML) and artificial intelligence (AI) algorithms are now being used to automate the discovery of physics principles and governing equations from measurement data alone.
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Automated reverse engineering of nonlinear dynamical systems
Josh Bongard and Hod Lipson · 2007
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Rabindra D Mehta · 2008
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Michael Schmidt and Hod Lipson · 2009
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Student learning experiences from drag experiments using high-speed video analysis
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Learning partial differential equations via data discovery and sparse optimization
Hayden Schaeffer · 2017
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Sparse model selection via integral terms
Hayden Schaeffer and Scott G McCalla · 2017
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Machine learning of linear differential equations using gaussian processes
Maziar Raissi, Paris Perdikaris, and George Em Karniadakis · 2017
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Process-based modeling and design of dynamical systems
Jovan Tanevski, Nikola Simidjievski, Ljupčo Todorovski, and Sašo Džeroski · 2017
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Maziar Raissi, Paris Perdikaris, and George Em Karniadakis · 2017
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Fluid Mechanics, 2011
Frank M White and RY Chul · 2011
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Machine learning: a probabilistic perspective
Kevin P Murphy · 2012
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A review of recent research into aerodynamics of sport projectiles
John Eric Goff · 2013
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The almagest: introduction to the mathematics of the heavens
Claudius Ptolemy · 2014
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Measuring the drag force on a falling ball
Rod Cross and Crawford Lindsey · 2014
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Laboratory test of the galilean universality of the free fall experiment
Rasmus S Christensen, Ricky Teiwes, Steffen V Petersen, Ulrik I Uggerhøj, and Bo Jacoby · 2014
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Maziar Raissi, Paris Perdikaris, and George Em Karniadakis · 2017
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Visual interaction networks: Learning a physics simulator from video
Nicholas Watters, Daniel Zoran, Theophane Weber, Peter Battaglia, Razvan Pascanu, and Andrea Tacchetti · 2017
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Teaching the falling ball problem with dimensional analysis
Josué Sznitman, Howard A Stone, Alexander J Smits, and James B Grotberg · 2017
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False discoveries occur early on the lasso path
Weijie Su, Małgorzata Bogdan, Emmanuel Candes, et al · 2017
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Model selection for dynamical systems via sparse regression and information criteria
Niall M Mangan, J Nathan Kutz, Steven L Brunton, and Joshua L Proctor · 2017
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Constrained sparse Galerkin regression
J.-C. Loiseau and S. L. Brunton · 2018
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Sparse reduced-order modeling: sensor-based dynamics to full-state estimation
J.-C. Loiseau, B. R. Noack, and S. L. Brunton · 2018
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Sparse identification of nonlinear dynamics for model predictive control in the low-data limit
Eurika Kaiser, J Nathan Kutz, and Steven L Brunton · 2018
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Extracting sparse high-dimensional dynamics from limited data
Hayden Schaeffer, Giang Tran, and Rachel Ward · 2018
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Flexible neural representation for physics prediction
Damian Mrowca, Chengxu Zhuang, Elias Wang, Nick Haber, Li F Fei-Fei, Josh Tenenbaum, and Daniel L Yamins · 2018
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Deep hidden physics models: Deep learning of nonlinear partial differential equations
Maziar Raissi · 2018
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Relational inductive biases, deep learning, and graph networks
Peter W Battaglia, Jessica B Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, et al · 2018
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A unified framework for sparse relaxed regularized regression: Sr3
Peng Zheng, Travis Askham, Steven L Brunton, J Nathan Kutz, and Aleksandr Y Aravkin · 2018
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Reactive SINDy: Discovering governing reactions from concentration data
Moritz Hoffmann, Christoph Fröhner, and Frank Noé · 2019
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Sparse structural system identification method for nonlinear dynamic systems with hysteresis/inelastic behavior
Zhilu Lai and Satish Nagarajaiah · 2019
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Data-driven identification of parametric partial differential equations
S. Rudy, A. Alla, S. L. Brunton, and J. N. Kutz · 2019
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A unified sparse optimization framework to learn parsimonious physics-informed models from data
Kathleen Champion, Peng Zheng, Aleksandr Y Aravkin, Steven L Brunton, and J Nathan Kutz · 2019
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