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Sparse model identification enables the discovery of nonlinear dynamical systems purely from data; however, this approach is sensitive to noise, especially in the low-data limit.
The conservation of the wild life of Canada
Charles Gordon Hewitt · 1921
Earlier work this paper cites.
Model predictive control: theory and practice—a survey
Carlos E Garcia, David M Prett, and Manfred Morari · 1989
Earlier work this paper cites.
The strength of weak learnability
Robert E Schapire · 1990
Earlier work this paper cites.
Boosting a weak learning algorithm by majority
Yoav Freund · 1995
Earlier work this paper cites.
Neural network ensembles, cross validation, and active learning
Anders Krogh, Jesper Vedelsby, et al · 1995
Earlier work this paper cites.
Bagging predictors
Leo Breiman · 1996
Earlier work this paper cites.
Model predictive control: past, present and future
Manfred Morari and Jay H Lee · 1999
Earlier work this paper cites.
The elements of statistical learning. springer series in statistics
Trevor Hastie, Robert Tibshirani, and Jerome Friedman · 2001
Earlier work this paper cites.
Equation-free, coarse-grained multiscale computation: Enabling mocroscopic simulators to perform system-level analysis
Ioannis G Kevrekidis, C William Gear, James M Hyman, Panagiotis G Kevrekidid, Olof Runborg, Constantinos Theodoropoulos, et al · 2003
Earlier work this paper cites.
Bagging, subagging and bragging for improving some prediction algorithms
Peter Lukas Bühlmann · 2003
Earlier work this paper cites.
Spectral properties of dynamical systems, model reduction and decompositions
Igor Mezić · 2005
Earlier work this paper cites.
Automated reverse engineering of nonlinear dynamical systems
Josh Bongard and Hod Lipson · 2007
Earlier work this paper cites.
Normal forms for reduced stochastic climate models
Andrew J Majda, Christian Franzke, and Daan Crommelin · 2009
Earlier work this paper cites.
Spectral analysis of nonlinear flows
Clarence W Rowley, Igor Mezić, Shervin Bagheri, Philipp Schlatter, and Dan S. Henningson · 2009
Earlier work this paper cites.
Distilling free-form natural laws from experimental data
Michael Schmidt and Hod Lipson · 2009
Earlier work this paper cites.
Active learning literature survey
Burr Settles · 2009
Earlier work this paper cites.
Nonlinear system modeling and predictive control using the RBF nets-based quasi-linear ARX model
Hui Peng, Jun Wu, Garba Inoussa, Qiulian Deng, and Kazushi Nakano · 2009
Earlier work this paper cites.
Dynamic mode decomposition of numerical and experimental data
Peter J. Schmid · 2010
Earlier work this paper cites.
Stability selection
Nicolai Meinshausen and Peter Bühlmann · 2010
Earlier work this paper cites.
Active learning from stream data using optimal weight classifier ensemble
Xingquan Zhu, Peng Zhang, Xiaodong Lin, and Yong Shi · 2010
Earlier work this paper cites.
Automated refinement and inference of analytical models for metabolic networks
Michael D Schmidt, Ravishankar R Vallabhajosyula, Jerry W Jenkins, Jonathan E Hood, Abhishek S Soni, John P Wikswo, and Hod Lipson · 2011
Earlier work this paper cites.
From theories to queries: Active learning in practice
Burr Settles · 2011
Earlier work this paper cites.
Nonlinear laplacian spectral analysis for time series with intermittency and low-frequency variability
Dimitrios Giannakis and Andrew J Majda · 2012
Earlier work this paper cites.
Bagging, boosting and ensemble methods
Peter Bühlmann · 2012
Earlier work this paper cites.
Analysis of fluid flows via spectral properties of the Koopman operator
Igor Mezic · 2013
Earlier work this paper cites.
Model predictive control: Recent developments and future promise
David Q. Mayne · 2014
Earlier work this paper cites.
Automated adaptive inference of phenomenological dynamical models
Bryan C Daniels and Ilya Nemenman · 2015
Earlier work this paper cites.
Efficient inference of parsimonious phenomenological models of cellular dynamics using s-systems and alternating regression
Bryan C Daniels and Ilya Nemenman · 2015
Earlier work this paper cites.
Dynamic Mode Decomposition: Data-Driven Modeling of Complex Systems
J. Nathan Kutz, Steven L. Brunton, Bingni W. Brunton, and Joshua L. Proctor · 2016
Earlier work this paper cites.
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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Sparse identification for nonlinear optical communication systems: SINO method
Mariia Sorokina, Stylianos Sygletos, and Sergei Turitsyn · 2016
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Tianhao Zhang, Gregory Kahn, Sergey Levine, and Pieter Abbeel · 2016
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Or Yair, Ronen Talmon, Ronald R Coifman, and Ioannis G Kevrekidis · 2017
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SINDy-PI: a robust algorithm for parallel implicit sparse identification of nonlinear dynamics
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Using noisy or incomplete data to discover models of spatiotemporal dynamics
Patrick AK Reinbold, Daniel R Gurevich, and Roman O Grigoriev · 2020
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Bayesian system id: Optimal management of parameter, model, and measurement uncertainty
Nicholas Galioto and Alex Arkady Gorodetsky · 2020
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Bayesian identification of dynamical systems
Robert K Niven, Ali Mohammad-Djafari, Laurent Cordier, Markus Abel, and Markus Quade · 2020
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Bayesian differential programming for robust systems identification under uncertainty
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