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Forecasting Sequential Data using Consistent Koopman Autoencoders

Forecasting Sequential Data using Consistent Koopman Autoencoders

4 March 2020
Omri Azencot
N. Benjamin Erichson
Vanessa Lin
Michael W. Mahoney
    AI4TS
    AI4CE
ArXivPDFHTML

Papers citing "Forecasting Sequential Data using Consistent Koopman Autoencoders"

27 / 27 papers shown
Title
Learning Koopman-based Stability Certificates for Unknown Nonlinear Systems
Learning Koopman-based Stability Certificates for Unknown Nonlinear Systems
Ruikun Zhou
Yiming Meng
Zhexuan Zeng
Jun Liu
75
0
0
03 Dec 2024
When Graph Neural Networks Meet Dynamic Mode Decomposition
When Graph Neural Networks Meet Dynamic Mode Decomposition
Dai Shi
Lequan Lin
Andi Han
Zhiyong Wang
Yi Guo
Junbin Gao
AI4CE
23
0
0
08 Oct 2024
Learning Interpretable Hierarchical Dynamical Systems Models from Time Series Data
Learning Interpretable Hierarchical Dynamical Systems Models from Time Series Data
Manuel Brenner
Elias Weber
G. Koppe
Daniel Durstewitz
AI4TS
AI4CE
36
2
0
07 Oct 2024
Elucidating the Design Choice of Probability Paths in Flow Matching for Forecasting
Elucidating the Design Choice of Probability Paths in Flow Matching for Forecasting
S. H. Lim
Yijin Wang
Annan Yu
Emma Hart
Michael W. Mahoney
Xiaoye S. Li
N. Benjamin Erichson
AI4TS
47
1
0
04 Oct 2024
Data-Driven Optimal Control of Tethered Space Robot Deployment with
  Learning Based Koopman Operator
Data-Driven Optimal Control of Tethered Space Robot Deployment with Learning Based Koopman Operator
Ao Jin
Fan Zhang
Panfeng Huang
22
3
0
15 Jul 2023
Koopa: Learning Non-stationary Time Series Dynamics with Koopman
  Predictors
Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors
Yong Liu
Chenyu Li
Jianmin Wang
Mingsheng Long
AI4TS
28
101
0
30 May 2023
Learning Linear Embeddings for Non-Linear Network Dynamics with Koopman
  Message Passing
Learning Linear Embeddings for Non-Linear Network Dynamics with Koopman Message Passing
King Fai Yeh
Paris D. L. Flood
William T. Redman
Pietro Lio'
22
1
0
15 May 2023
Multifactor Sequential Disentanglement via Structured Koopman
  Autoencoders
Multifactor Sequential Disentanglement via Structured Koopman Autoencoders
Nimrod Berman
Ilana D Naiman
Omri Azencot
CoGe
22
21
0
30 Mar 2023
A polar prediction model for learning to represent visual
  transformations
A polar prediction model for learning to represent visual transformations
P. Fiquet
Eero P. Simoncelli
28
4
0
06 Mar 2023
KoopmanLab: machine learning for solving complex physics equations
KoopmanLab: machine learning for solving complex physics equations
Wei Xiong
Muyuan Ma
Xiaomeng Huang
Ziyang Zhang
Pei Sun
Yang Tian
AI4CE
26
13
0
03 Jan 2023
Physics-Informed Koopman Network
Physics-Informed Koopman Network
Yuying Liu
A. Sholokhov
Hassan Mansour
S. Nabi
AI4CE
25
3
0
17 Nov 2022
DLKoopman: A deep learning software package for Koopman theory
DLKoopman: A deep learning software package for Koopman theory
Sourya Dey
Eric K. Davis
AI4CE
19
3
0
15 Nov 2022
Koopman Neural Forecaster for Time Series with Temporal Distribution
  Shifts
Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts
Rui Wang
Yihe Dong
Sercan Ö. Arik
Rose Yu
AI4TS
31
22
0
07 Oct 2022
Parameter-varying neural ordinary differential equations with
  partition-of-unity networks
Parameter-varying neural ordinary differential equations with partition-of-unity networks
Kookjin Lee
N. Trask
22
2
0
01 Oct 2022
A Review of Machine Learning Methods Applied to Structural Dynamics and
  Vibroacoustic
A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic
Barbara Z Cunha
C. Droz
A. Zine
Stéphane Foulard
M. Ichchou
AI4CE
29
84
0
13 Apr 2022
When Physics Meets Machine Learning: A Survey of Physics-Informed
  Machine Learning
When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learning
Chuizheng Meng
Sungyong Seo
Defu Cao
Sam Griesemer
Yan Liu
PINN
AI4CE
39
55
0
31 Mar 2022
Learning continuous models for continuous physics
Learning continuous models for continuous physics
Aditi S. Krishnapriyan
A. Queiruga
N. Benjamin Erichson
Michael W. Mahoney
AI4CE
24
33
0
17 Feb 2022
Deep Koopman Operator with Control for Nonlinear Systems
Deep Koopman Operator with Control for Nonlinear Systems
Hao-bin Shi
M. Meng
12
74
0
16 Feb 2022
Bounded nonlinear forecasts of partially observed geophysical systems
  with physics-constrained deep learning
Bounded nonlinear forecasts of partially observed geophysical systems with physics-constrained deep learning
Said Ouala
Steven L. Brunton
A. Pascual
Bertrand Chapron
F. Collard
L. Gaultier
Ronan Fablet
PINN
AI4TS
AI4CE
18
10
0
11 Feb 2022
SyMetric: Measuring the Quality of Learnt Hamiltonian Dynamics Inferred
  from Vision
SyMetric: Measuring the Quality of Learnt Hamiltonian Dynamics Inferred from Vision
I. Higgins
Peter Wirnsberger
Andrew Jaegle
Aleksandar Botev
37
7
0
10 Nov 2021
Deep Learning Enhanced Dynamic Mode Decomposition
Deep Learning Enhanced Dynamic Mode Decomposition
D. J. Alford-Lago
C. Curtis
Alexander T. Ihler
Opal Issan
24
33
0
10 Aug 2021
Physics-Guided Deep Learning for Dynamical Systems: A Survey
Physics-Guided Deep Learning for Dynamical Systems: A Survey
Rui Wang
Rose Yu
AI4CE
PINN
39
64
0
02 Jul 2021
Meta-Learning Dynamics Forecasting Using Task Inference
Meta-Learning Dynamics Forecasting Using Task Inference
Rui Wang
Robin G. Walters
Rose Yu
OOD
AI4TS
AI4CE
24
31
0
20 Feb 2021
Lipschitz Recurrent Neural Networks
Lipschitz Recurrent Neural Networks
N. Benjamin Erichson
Omri Azencot
A. Queiruga
Liam Hodgkinson
Michael W. Mahoney
28
107
0
22 Jun 2020
A Tailored Convolutional Neural Network for Nonlinear Manifold Learning
  of Computational Physics Data using Unstructured Spatial Discretizations
A Tailored Convolutional Neural Network for Nonlinear Manifold Learning of Computational Physics Data using Unstructured Spatial Discretizations
John Tencer
Kevin Potter
AI4CE
15
13
0
11 Jun 2020
Symplectic Recurrent Neural Networks
Symplectic Recurrent Neural Networks
Zhengdao Chen
Jianyu Zhang
Martín Arjovsky
Léon Bottou
146
219
0
29 Sep 2019
Geometric deep learning: going beyond Euclidean data
Geometric deep learning: going beyond Euclidean data
M. Bronstein
Joan Bruna
Yann LeCun
Arthur Szlam
P. Vandergheynst
GNN
250
3,236
0
24 Nov 2016
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