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Title: GitHub - jbramburger/DataDrivenDynSyst: Scripts and notebooks to accompany the book Data-Driven Methods for Dynamic Systems · GitHub

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Description: Scripts and notebooks to accompany the book Data-Driven Methods for Dynamic Systems - jbramburger/DataDrivenDynSyst

Open Graph Description: Scripts and notebooks to accompany the book Data-Driven Methods for Dynamic Systems - jbramburger/DataDrivenDynSyst

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Autoencoder Neural Networkshttps://github.com/jbramburger/DataDrivenDynSyst/tree/main/Autoencoder%20Neural%20Networks
Autoencoder Neural Networkshttps://github.com/jbramburger/DataDrivenDynSyst/tree/main/Autoencoder%20Neural%20Networks
Data-Driven Polynomial Optimizationhttps://github.com/jbramburger/DataDrivenDynSyst/tree/main/Data-Driven%20Polynomial%20Optimization
Data-Driven Polynomial Optimizationhttps://github.com/jbramburger/DataDrivenDynSyst/tree/main/Data-Driven%20Polynomial%20Optimization
Dynamical Systems Old and Newhttps://github.com/jbramburger/DataDrivenDynSyst/tree/main/Dynamical%20Systems%20Old%20and%20New
Dynamical Systems Old and Newhttps://github.com/jbramburger/DataDrivenDynSyst/tree/main/Dynamical%20Systems%20Old%20and%20New
Identifying Nonlinear Dynamicshttps://github.com/jbramburger/DataDrivenDynSyst/tree/main/Identifying%20Nonlinear%20Dynamics
Identifying Nonlinear Dynamicshttps://github.com/jbramburger/DataDrivenDynSyst/tree/main/Identifying%20Nonlinear%20Dynamics
Learning Dynamics with Neural Networkshttps://github.com/jbramburger/DataDrivenDynSyst/tree/main/Learning%20Dynamics%20with%20Neural%20Networks
Learning Dynamics with Neural Networkshttps://github.com/jbramburger/DataDrivenDynSyst/tree/main/Learning%20Dynamics%20with%20Neural%20Networks
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https://github.com/jbramburger/DataDrivenDynSyst#data-driven-methods-for-dynamic-systems
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Dynamical Systems: Old and Newhttps://github.com/jbramburger/DataDrivenDynSyst/tree/main/Dynamical%20Systems%20Old%20and%20New
Linear Evolution Modelshttps://github.com/jbramburger/DataDrivenDynSyst/tree/main/Linear%20Evolution%20Models
windowed DMD method of Dylewsky et al.https://journals.aps.org/pre/abstract/10.1103/PhysRevE.99.063311
Extended Dynamic Mode Decompositionhttps://link.springer.com/article/10.1007/s00332-015-9258-5
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Identifying Nonlinear Dynamicshttps://github.com/jbramburger/DataDrivenDynSyst/tree/main/Identifying%20Nonlinear%20Dynamics
sparse identification of nonlinear dynamics method (SINDy)https://www.pnas.org/doi/10.1073/pnas.1517384113
Schaeffer and McCallahttps://pubmed.ncbi.nlm.nih.gov/28950639/
Bramburger and Kutzhttps://www.sciencedirect.com/science/article/pii/S0167278919305470
control of chaos methodhttps://journals.aps.org/prl/abstract/10.1103/PhysRevLett.64.1196
Bramburger, Dylewsky, & Kutzhttps://journals.aps.org/pre/abstract/10.1103/PhysRevE.102.022204
Kaiser et al.http://eurika-kaiser.com/downloads/KaKuBr2018cdc.pdf
Data-Driven Polynomial Optimizationhttps://github.com/jbramburger/DataDrivenDynSyst/tree/main/Data-Driven%20Polynomial%20Optimization
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Learning Dynamics with Neural Networkshttps://github.com/jbramburger/DataDrivenDynSyst/tree/main/Learning%20Dynamics%20with%20Neural%20Networks
physics informed neural network (PINN)https://www.sciencedirect.com/science/article/pii/S0021999118307125
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