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Linearly-Recurrent Autoencoder Networks for Learning Dynamics

4 December 2017

Papers citing "Linearly-Recurrent Autoencoder Networks for Learning Dynamics"

24 / 124 papers shown

Title
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
Reimagining City Configuration: Automated Urban Planning via Adversarial Learning Dongjie Wang Yanjie Fu Pengyang Wang B. Huang Chang-Tien Lu 110 30 0 22 Aug 2020
A Tailored Convolutional Neural Network for Nonlinear Manifold Learning of Computational Physics Data using Unstructured Spatial Discretizations John Tencer Kevin Potter AI4CE 61 13 0 11 Jun 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
Sparsity-promoting algorithms for the discovery of informative Koopman invariant subspaces Shaowu Pan Nicholas Arnold-Medabalimi Karthik Duraisamy 78 48 0 25 Feb 2020
Deep reconstruction of strange attractors from time series W. Gilpin AI4TS 56 3 0 14 Feb 2020
Deep learning to discover and predict dynamics on an inertial manifold Alec J. Linot M. Graham AI4CE 80 75 0 20 Dec 2019
Parameter-Conditioned Sequential Generative Modeling of Fluid Flows Jeremy Morton F. Witherden Mykel J. Kochenderfer GAN MedIm AI4CE 61 10 0 14 Dec 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
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
Physics-Informed Probabilistic Learning of Linear Embeddings of Non-linear Dynamics With Guaranteed Stability Shaowu Pan Karthik Duraisamy 140 138 0 09 Jun 2019
A Discrete Empirical Interpolation Method for Interpretable Immersion and Embedding of Nonlinear Manifolds Samuel E. Otto C. Rowley 20 3 0 18 May 2019
Kernel methods for detecting coherent structures in dynamical data Stefan Klus B. Husic Mattes Mollenhauer Frank Noé 57 29 0 16 Apr 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
Machine Learning for Molecular Dynamics on Long Timescales Frank Noé AI4CE 77 32 0 18 Dec 2018
Deep convolutional recurrent autoencoders for learning low-dimensional feature dynamics of fluid systems F. J. Gonzalez Maciej Balajewicz AI4CE 152 140 0 03 Aug 2018
Discovering physical concepts with neural networks Raban Iten Tony Metger H. Wilming L. D. Rio R. Renner PINN AI4CE 129 391 0 26 Jul 2018
Deep Generative Markov State Models Hao Wu Andreas Mardt Luca Pasquali Frank Noe AI4CE 81 60 0 19 May 2018
Deep learning for universal linear embeddings of nonlinear dynamics Bethany Lusch J. Nathan Kutz Steven L. Brunton 121 1,268 0 27 Dec 2017
Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition Naoya Takeishi Yoshinobu Kawahara Takehisa Yairi 86 374 0 12 Oct 2017
Learning Deep Neural Network Representations for Koopman Operators of Nonlinear Dynamical Systems Enoch Yeung Soumya Kundu Nathan Oken Hodas AI4CE 92 387 0 22 Aug 2017
Variational approach for learning Markov processes from time series data Hao Wu Frank Noé BDL AI4TS 92 266 0 14 Jul 2017