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Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition

12 October 2017

Papers citing "Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition"

50 / 151 papers shown

Title
Deep Probabilistic Koopman: Long-term time-series forecasting under periodic uncertainties Alex Troy Mallen Henning Lange J. Nathan Kutz AI4TS 63 8 0 10 Jun 2021
The kernel perspective on dynamic mode decomposition Efrain Gonzalez Moad Abudia M. Jury R. Kamalapurkar Joel A. Rosenfeld 23 7 0 31 May 2021
Operator Autoencoders: Learning Physical Operations on Encoded Molecular Graphs Willis Hoke D. Shea S. Casey AI4CE 57 0 0 26 May 2021
A Differential Geometry Perspective on Orthogonal Recurrent Models Omri Azencot N. Benjamin Erichson M. Ben-Chen Michael W. Mahoney AI4CE 48 5 0 18 Feb 2021
An Operator Theoretic Approach for Analyzing Sequence Neural Networks Ilana D Naiman Omri Azencot 108 11 0 15 Feb 2021
Data-driven Analysis for Understanding Team Sports Behaviors Keisuke Fujii AI4TS AI4CE 71 27 0 15 Feb 2021
Noisy Recurrent Neural Networks Soon Hoe Lim N. Benjamin Erichson Liam Hodgkinson Michael W. Mahoney 93 54 0 09 Feb 2021
Meta-Learning for Koopman Spectral Analysis with Short Time-series Tomoharu Iwata Yoshinobu Kawahara AI4TS 57 0 0 09 Feb 2021
Reduced operator inference for nonlinear partial differential equations E. Qian Ionut-Gabriel Farcas Karen E. Willcox AI4CE 65 39 0 29 Jan 2021
Reproducing kernel Hilbert C*-module and kernel mean embeddings Yuka Hashimoto Isao Ishikawa Masahiro Ikeda Fuyuta Komura Takeshi Katsura Yoshinobu Kawahara 35 11 0 27 Jan 2021
DeepGreen: Deep Learning of Green's Functions for Nonlinear Boundary Value Problems Craig Gin D. Shea Steven L. Brunton J. Nathan Kutz 92 90 0 31 Dec 2020
Neural Dynamic Mode Decomposition for End-to-End Modeling of Nonlinear Dynamics Tomoharu Iwata Yoshinobu Kawahara 63 12 0 11 Dec 2020
Parameterized Neural Ordinary Differential Equations: Applications to Computational Physics Problems Kookjin Lee E. Parish 71 68 0 28 Oct 2020
Stochastic embeddings of dynamical phenomena through variational autoencoders C. A. García P. Félix J. Presedo A. Otero BDL 58 2 0 13 Oct 2020
Derivative-Based Koopman Operators for Real-Time Control of Robotic Systems Giorgos Mamakoukas Maria L. Castaño Xiaobo Tan Todd Murphey 71 92 0 12 Oct 2020
Transformers for Modeling Physical Systems N. Geneva N. Zabaras AI4CE 119 146 0 04 Oct 2020
Analyzing Koopman approaches to physics-informed machine learning for long-term sea-surface temperature forecasting Julian Rice Wenwei Xu Andrew August 50 10 0 15 Sep 2020
OnsagerNet: Learning Stable and Interpretable Dynamics using a Generalized Onsager Principle Haijun Yu Xinyuan Tian Weinan E Qianxiao Li AI4CE 112 44 0 06 Sep 2020
Deep Neural Networks for Nonlinear Model Order Reduction of Unsteady Flows Hamidreza Eivazi H. Veisi M. H. Naderi V. Esfahanian AI4CE 94 173 0 02 Jul 2020
Learning Dynamics Models with Stable Invariant Sets Naoya Takeishi Yoshinobu Kawahara 62 18 0 16 Jun 2020
Deep Adversarial Koopman Model for Reaction-Diffusion systems K. Balakrishnan Devesh Upadhyay 70 5 0 09 Jun 2020
Optimizing Neural Networks via Koopman Operator Theory Akshunna S. Dogra William T. Redman 78 51 0 03 Jun 2020
Hybrid Scheme of Kinematic Analysis and Lagrangian Koopman Operator Analysis for Short-term Precipitation Forecasting Shitao Zheng T. Miyamoto K. Iwanami S. Shimizu Ryohei Kato 71 3 0 03 Jun 2020
Learning Stable Models for Prediction and Control Giorgos Mamakoukas Ian Abraham Todd Murphey 97 39 0 08 May 2020
Recurrent neural networks and Koopman-based frameworks for temporal predictions in a low-order model of turbulence Hamidreza Eivazi L. Guastoni P. Schlatter Hossein Azizpour Ricardo Vinuesa AI4CE 60 7 0 01 May 2020
From Fourier to Koopman: Spectral Methods for Long-term Time Series Prediction Henning Lange Steven L. Brunton N. Kutz AI4TS 89 80 0 01 Apr 2020
Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems J. Willard X. Jia Shaoming Xu M. Steinbach Vipin Kumar AI4CE 158 415 0 10 Mar 2020
Bayesian System ID: Optimal management of parameter, model, and measurement uncertainty Nicholas Galioto Alex Gorodetsky 63 32 0 04 Mar 2020
Forecasting Sequential Data using Consistent Koopman Autoencoders Omri Azencot N. Benjamin Erichson Vanessa Lin Michael W. Mahoney AI4TS AI4CE 206 152 0 04 Mar 2020
Analysis via Orthonormal Systems in Reproducing Kernel Hilbert $C^*$ -Modules and Applications Yuka Hashimoto Isao Ishikawa Masahiro Ikeda Fuyuta Komura Takeshi Katsura Yoshinobu Kawahara 17 2 0 02 Mar 2020
Sparsity-promoting algorithms for the discovery of informative Koopman invariant subspaces Shaowu Pan Nicholas Arnold-Medabalimi Karthik Duraisamy 76 48 0 25 Feb 2020
Deep reconstruction of strange attractors from time series W. Gilpin AI4TS 53 3 0 14 Feb 2020
Universal Differential Equations for Scientific Machine Learning Christopher Rackauckas Yingbo Ma Julius Martensen Collin Warner K. Zubov R. Supekar Dominic J. Skinner Ali Ramadhan Alan Edelman AI4CE 117 599 0 13 Jan 2020
Composition operators on reproducing kernel Hilbert spaces with analytic positive definite functions Masahiro Ikeda Isao Ishikawa Y. Sawano CoGe 36 16 0 27 Nov 2019
Deep Learning Models for Global Coordinate Transformations that Linearize PDEs Craig Gin Bethany Lusch Steven L. Brunton J. Nathan Kutz 83 41 0 07 Nov 2019
Learning Compositional Koopman Operators for Model-Based Control Yunzhu Li Hao He Jiajun Wu Dina Katabi Antonio Torralba 112 121 0 18 Oct 2019
Towards Scalable Koopman Operator Learning: Convergence Rates and A Distributed Learning Algorithm Zhiyuan Liu Guohui Ding Lijun Chen Enoch Yeung 25 3 0 30 Sep 2019
A unified sparse optimization framework to learn parsimonious physics-informed models from data Kathleen P. Champion P. Zheng Aleksandr Aravkin Steven L. Brunton J. Nathan Kutz AI4CE 76 119 0 25 Jun 2019
Physics-Informed Probabilistic Learning of Linear Embeddings of Non-linear Dynamics With Guaranteed Stability Shaowu Pan Karthik Duraisamy 140 138 0 09 Jun 2019
Machine Learning for Fluid Mechanics Steven Brunton B. R. Noack Petros Koumoutsakos AI4CE PINN 103 2,146 0 27 May 2019
Physics-informed Autoencoders for Lyapunov-stable Fluid Flow Prediction N. Benjamin Erichson Michael Muehlebach Michael W. Mahoney AI4CE PINN 75 141 0 26 May 2019
Time-varying Autoregression with Low Rank Tensors K. Harris Aleksandr Aravkin Rajesh P. N. Rao Bingni W. Brunton AI4TS 80 20 0 21 May 2019
Physically-interpretable classification of biological network dynamics for complex collective motions Keisuke Fujii Naoya Takeishi Motokazu Hojo Yuki Inaba Yoshinobu Kawahara 110 11 0 13 May 2019
Deep Variational Koopman Models: Inferring Koopman Observations for Uncertainty-Aware Dynamics Modeling and Control Jeremy Morton F. Witherden Mykel J Kochenderfer 85 47 0 26 Feb 2019
Dynamic mode decomposition in vector-valued reproducing kernel Hilbert spaces for extracting dynamical structure among observables Keisuke Fujii Yoshinobu Kawahara 73 31 0 30 Aug 2018
Discovering physical concepts with neural networks Raban Iten Tony Metger H. Wilming L. D. Rio R. Renner PINN AI4CE 127 391 0 26 Jul 2018
Deep Dynamical Modeling and Control of Unsteady Fluid Flows Jeremy Morton F. Witherden A. Jameson Mykel J. Kochenderfer AI4CE 79 165 0 18 May 2018
Deep learning for universal linear embeddings of nonlinear dynamics Bethany Lusch J. Nathan Kutz Steven L. Brunton 92 1,268 0 27 Dec 2017
Linearly-Recurrent Autoencoder Networks for Learning Dynamics Samuel E. Otto C. Rowley AI4CE 86 328 0 04 Dec 2017
Learning Deep Neural Network Representations for Koopman Operators of Nonlinear Dynamical Systems Enoch Yeung Soumya Kundu Nathan Oken Hodas AI4CE 85 387 0 22 Aug 2017