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We consider the data-driven discovery of governing equations from time-series data in the limit of high noise.
Least median of squares regression
Rousseeuw, P. J · 1984
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Numerical differentiation of experimental data: local versus global methods
Ahnert, K. and Abel, M · 2007
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Automated adaptive inference of phenomenological dynamical models
Daniels, B. C. and Nemenman, I · 2015
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False Discoveries Occur Early on the Lasso Path
Su, W., Bogdan, M., and Candes, E · 2015
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Inferring biological networks by sparse identification of nonlinear dynamics
Mangan, N. M., Brunton, S. L., Proctor, J. L., and Kutz, J. N · 2016
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Sparse identification for nonlinear optical communication systems: SINO method
Sorokina, M., Sygletos, S., and Turitsyn, S · 2016
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Chaos as an intermittently forced linear system
Brunton, S. L., Brunton, B. W., Proctor, J. L., Kaiser, E., and Kutz, J. N · 2017
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Sparse identification of a predator-prey system from simulation data of a convection model
Dam, M., Brøns, M., Juul Rasmussen, J., Naulin, V., and Hesthaven, J. S · 2017
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Model selection for dynamical systems via sparse regression and information criteria
Mangan, N. M., Kutz, J. N., Brunton, S. L., and Proctor, J. L · 2017
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Data-driven discovery of partial differential equations
Rudy, S. H., Brunton, S. L., Proctor, J. L., and Kutz, J. N · 2017
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Learning partial differential equations via data discovery and sparse optimization
Schaeffer, H · 2017
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Sparse model selection via integral terms
Schaeffer, H. and McCalla, S. G · 2017
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Exact recovery of chaotic systems from highly corrupted data
Tran, G. and Ward, R · 2017
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Sparse learning of stochastic dynamical equations
Boninsegna, L., Nüske, F., and Clementi, C · 2018
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Constrained sparse Galerkin regression
Loiseau, J.-C. and Brunton, S. L · 2018
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Sparse reduced-order modeling: sensor-based dynamics to full-state estimation
Loiseau, J.-C., Noack, B. R., and Brunton, S. L · 2018
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Sparse identification of nonlinear dynamics for rapid model recovery
Quade, M., Abel, M., Nathan Kutz, J., and Brunton, S. L · 2018
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Robust data-driven discovery of governing physical laws with error bars
Zhang, S. and Lin, G · 2018
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Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control
Brunton, S. L. and Kutz, J. N · 2019
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Discovery of physics from data: Universal laws and discrepancy models
de Silva, B., Higdon, D. M., Brunton, S. L., and Kutz, J. N · 2019
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Multidimensional approximation of nonlinear dynamical systems
Gelß, P., Klus, S., Eisert, J., and Schütte, C · 2019
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Robust and optimal sparse regression for nonlinear pde models
SINDy-PI: a robust algorithm for parallel implicit sparse identification of nonlinear dynamics
Kaheman, K., Kutz, J. N., and Brunton, S. L · 2020
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Data-driven modeling of the chaotic thermal convection in an annular thermosyphon
Loiseau, J.-C · 2020
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Using noisy or incomplete data to discover models of spatiotemporal dynamics
Reinbold, P. A., Gurevich, D. R., and Grigoriev, R. O · 2020
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Discovery of algebraic reynolds-stress models using sparse symbolic regression
Schmelzer, M., Dwight, R. P., and Cinnella, P · 2020
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Sparse identification of multiphase turbulence closures for coupled fluid–particle flows
Beetham, S., Fox, R. O., and Capecelatro, J · 2021
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Data-driven stabilization of periodic orbits
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Gurevich, D. R., Reinbold, P. A., and Grigoriev, R. O · 2019
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Learning discrepancy models from experimental data
Kaheman, K., Kaiser, E., Strom, B., Kutz, J. N., and Brunton, S. L · 2019
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Smoothing and parameter estimation by soft-adherence to governing equations
Rudy, S. H., Brunton, S. L., and Kutz, J. N · 2019
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Sparse identification of truncation errors
Thaler, S., Paehler, L., and Adams, N. A · 2019
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On the convergence of the sindy algorithm
Zhang, L. and Schaeffer, H · 2019
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Data-driven discovery of reduced plasma physics models from fully-kinetic simulations
Alves, E. P. and Fiuza, F · 2020
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Formulating turbulence closures using sparse regression with embedded form invariance
Beetham, S. and Capecelatro, J · 2020
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Bramburger, J. J., Kutz, J. N., and Brunton, S. L · 2021
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Codebase for High Noise SINDy Toolkit
Delahunt, C. B. and Kutz, J. N · 2021
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Galerkin force model for transient and post-transient dynamics of the fluidic pinball
Deng, N., Noack, B. R., Morzyński, M., and Pastur, L. R · 2021
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Ensemble-SINDy: Model discovery in the low-data, high-noise limit
Fasel, U., Kutz, J., Brunton, B., and Brunton, S · 2021
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Sparse nonlinear models of chaotic electroconvection
Guan, Y., Brunton, S. L., and Novosselov, I · 2021
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Sparsifying priors for bayesian inference/uncertainty quantification in system identification
Hirsh, S. M · 2021
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Kalia, M., Brunton, S. L., Meijer, H. G., Brune, C., and Kutz, J. N · 2021
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Physics-constrained, low-dimensional models for mhd: First-principles and data-driven approaches
Kaptanoglu, A. A., Morgan, K. D., Hansen, C. J., and Brunton, S. L · 2021
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Robust learning from noisy, incomplete, high-dimensional experimental data via physically constrained symbolic regression
Reinbold, P. A., Kageorge, L. M., Schatz, M. F., and Grigoriev, R. O · 2021
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Sindy-bvp: Sparse identification of nonlinear dynamics for boundary value problems
Shea, D. E., Brunton, S. L., and Kutz, J. N · 2021
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