PyKoopman: A Python Package for Data-Driven Approximation of the Koopman Operator

22 June 2023

Steven L. Brunton

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Papers citing "PyKoopman: A Python Package for Data-Driven Approximation of the Koopman Operator"

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Title
CGKN: A Deep Learning Framework for Modeling Complex Dynamical Systems and Efficient Data Assimilation Chuanqi Chen Nan Chen Yinling Zhang Jin-Long Wu AI4CE 83 2 0 26 Oct 2024
Balanced Neural ODEs: nonlinear model order reduction and Koopman operator approximations Julius Aka Johannes Brunnemann Jörg Eiden Arne Speerforck Lars Mikelsons 98 0 0 14 Oct 2024
Koopman Operators in Robot Learning Lu Shi Masih Haseli Giorgos Mamakoukas Daniel Bruder Ian Abraham Todd Murphey Jorge Cortes Konstantinos Karydis AI4CE 111 8 0 08 Aug 2024
The mpEDMD Algorithm for Data-Driven Computations of Measure-Preserving Dynamical Systems Matthew J. Colbrook 81 34 0 06 Sep 2022
Residual Dynamic Mode Decomposition: Robust and verified Koopmanism Matthew J. Colbrook Lorna J. Ayton Máté Szőke 65 62 0 19 May 2022
PySINDy: A comprehensive Python package for robust sparse system identification A. Kaptanoglu Brian M. de Silva Urban Fasel Kadierdan Kaheman Andy J. Goldschmidt ... Zachary G. Nicolaou Kathleen P. Champion Jean-Christophe Loiseau J. Nathan Kutz Steven L. Brunton AI4CE 100 150 0 12 Nov 2021
Deeptime: a Python library for machine learning dynamical models from time series data Moritz Hoffmann Martin K. Scherer Tim Hempel Andreas Mardt Brian M. de Silva ... Stefan Klus Hao Wu N. Kutz Steven L. Brunton Frank Noé AI4CE 88 106 0 28 Oct 2021
Modern Koopman Theory for Dynamical Systems Steven L. Brunton M. Budišić E. Kaiser J. Nathan Kutz AI4CE 122 420 0 24 Feb 2021
Advantages of Bilinear Koopman Realizations for the Modeling and Control of Systems with Unknown Dynamics Daniel Bruder Xun Fu Ram Vasudevan 51 81 0 20 Oct 2020
From Fourier to Koopman: Spectral Methods for Long-term Time Series Prediction Henning Lange Steven L. Brunton N. Kutz AI4TS 77 80 0 01 Apr 2020
Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems E. Qian Boris Kramer Benjamin Peherstorfer Karen E. Willcox AI4CE 111 267 0 17 Dec 2019
DeepXDE: A deep learning library for solving differential equations Lu Lu Xuhui Meng Zhiping Mao George Karniadakis PINN AI4CE 101 1,549 0 10 Jul 2019
Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations Siyu Dai Shawn Schaffert Andreas G. Hofmann 134 367 0 05 Nov 2018
Deep learning for universal linear embeddings of nonlinear dynamics Bethany Lusch J. Nathan Kutz Steven L. Brunton 83 1,261 0 27 Dec 2017
Linearly-Recurrent Autoencoder Networks for Learning Dynamics Samuel E. Otto C. Rowley AI4CE 74 328 0 04 Dec 2017
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics C. Wehmeyer Frank Noé AI4CE BDL 167 361 0 30 Oct 2017
PDE-Net: Learning PDEs from Data Zichao Long Yiping Lu Xianzhong Ma Bin Dong DiffM AI4CE 75 761 0 26 Oct 2017
VAMPnets: Deep learning of molecular kinetics Andreas Mardt Luca Pasquali Hao Wu Frank Noé 70 546 0 16 Oct 2017
Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition Naoya Takeishi Yoshinobu Kawahara Takehisa Yairi 51 374 0 12 Oct 2017
Parametric Gaussian Process Regression for Big Data M. Raissi 86 39 0 11 Apr 2017