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Adaptive learning of effective dynamics: Adaptive real-time, online
  modeling for complex systems

Adaptive learning of effective dynamics: Adaptive real-time, online modeling for complex systems

4 April 2023
Ivica Kicic
Pantelis R. Vlachas
G. Arampatzis
Michail Chatzimanolakis
Leonidas Guibas
Petros Koumoutsakos
    AI4CE
ArXiv (abs)PDFHTML

Papers citing "Adaptive learning of effective dynamics: Adaptive real-time, online modeling for complex systems"

34 / 34 papers shown
Title
The transformative potential of machine learning for experiments in
  fluid mechanics
The transformative potential of machine learning for experiments in fluid mechanics
Ricardo Vinuesa
Steven L. Brunton
B. McKeon
AI4CE
57
75
0
28 Mar 2023
Learning to Accelerate Partial Differential Equations via Latent Global
  Evolution
Learning to Accelerate Partial Differential Equations via Latent Global Evolution
Tailin Wu
Takashi Maruyama
J. Leskovec
AI4CE
70
33
0
15 Jun 2022
gLaSDI: Parametric Physics-informed Greedy Latent Space Dynamics
  Identification
gLaSDI: Parametric Physics-informed Greedy Latent Space Dynamics Identification
Xiaolong He
Youngsoo Choi
William D. Fries
Jonathan Belof
Jiun-Shyan Chen
AI4CE
47
39
0
26 Apr 2022
Continual Test-Time Domain Adaptation
Continual Test-Time Domain Adaptation
Qin Wang
Olga Fink
Luc Van Gool
Dengxin Dai
OODTTA
110
431
0
25 Mar 2022
Learned Coarse Models for Efficient Turbulence Simulation
Learned Coarse Models for Efficient Turbulence Simulation
Kimberly L. Stachenfeld
D. Fielding
Dmitrii Kochkov
M. Cranmer
Tobias Pfaff
Jonathan Godwin
Can Cui
S. Ho
Peter W. Battaglia
Alvaro Sanchez-Gonzalez
AI4CE
98
84
0
31 Dec 2021
AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification
AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification
Romain Egele
R. Maulik
Krishnan Raghavan
Bethany Lusch
Isabelle M Guyon
Prasanna Balaprakash
UQCVOODBDL
125
48
0
26 Oct 2021
Enhancing Computational Fluid Dynamics with Machine Learning
Enhancing Computational Fluid Dynamics with Machine Learning
Ricardo Vinuesa
Steven L. Brunton
AI4CE
169
377
0
05 Oct 2021
Data-driven discovery of intrinsic dynamics
Data-driven discovery of intrinsic dynamics
D. Floryan
M. Graham
AI4CE
149
77
0
12 Aug 2021
Deep Learning for Reduced Order Modelling and Efficient Temporal
  Evolution of Fluid Simulations
Deep Learning for Reduced Order Modelling and Efficient Temporal Evolution of Fluid Simulations
Pranshu Pant
Ruchi Doshi
Pranav Bahl
A. Farimani
AI4CE
56
83
0
09 Jul 2021
Autoformer: Decomposition Transformers with Auto-Correlation for
  Long-Term Series Forecasting
Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting
Haixu Wu
Jiehui Xu
Jianmin Wang
Mingsheng Long
AI4TS
113
2,296
0
24 Jun 2021
Machine learning accelerated computational fluid dynamics
Machine learning accelerated computational fluid dynamics
Dmitrii Kochkov
Jamie A. Smith
Ayya Alieva
Qing Wang
M. Brenner
Stephan Hoyer
AI4CE
135
874
0
28 Jan 2021
POD-DL-ROM: enhancing deep learning-based reduced order models for
  nonlinear parametrized PDEs by proper orthogonal decomposition
POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition
S. Fresca
Andrea Manzoni
AI4CE
67
217
0
28 Jan 2021
A Probabilistic Graphical Model Foundation for Enabling Predictive
  Digital Twins at Scale
A Probabilistic Graphical Model Foundation for Enabling Predictive Digital Twins at Scale
Michael G. Kapteyn
Jacob V. R. Pretorius
Karen E. Willcox
59
227
0
10 Dec 2020
Applying Convolutional Neural Networks to Data on Unstructured Meshes
  with Space-Filling Curves
Applying Convolutional Neural Networks to Data on Unstructured Meshes with Space-Filling Curves
Claire E. Heaney
Yuling Li
Omar K. Matar
Christopher C. Pain
AI4CE
64
17
0
24 Nov 2020
Dynamical Variational Autoencoders: A Comprehensive Review
Dynamical Variational Autoencoders: A Comprehensive Review
Laurent Girin
Simon Leglaive
Xiaoyu Bie
Julien Diard
Thomas Hueber
Xavier Alameda-Pineda
BDL
99
219
0
28 Aug 2020
Hierarchical Deep Learning of Multiscale Differential Equation
  Time-Steppers
Hierarchical Deep Learning of Multiscale Differential Equation Time-Steppers
Yuying Liu
N. Kutz
Steven L. Brunton
AI4TS
47
78
0
22 Aug 2020
Deep Neural Networks for Nonlinear Model Order Reduction of Unsteady
  Flows
Deep Neural Networks for Nonlinear Model Order Reduction of Unsteady Flows
Hamidreza Eivazi
H. Veisi
M. H. Naderi
V. Esfahanian
AI4CE
66
173
0
02 Jul 2020
Multiscale Simulations of Complex Systems by Learning their Effective
  Dynamics
Multiscale Simulations of Complex Systems by Learning their Effective Dynamics
Pantelis R. Vlachas
G. Arampatzis
Caroline Uhler
Petros Koumoutsakos
AI4CE
58
150
0
24 Jun 2020
Decision-Making with Auto-Encoding Variational Bayes
Decision-Making with Auto-Encoding Variational Bayes
Romain Lopez
Pierre Boyeau
Nir Yosef
Michael I. Jordan
Jeffrey Regier
BDL
521
10,591
0
17 Feb 2020
Backpropagation Algorithms and Reservoir Computing in Recurrent Neural
  Networks for the Forecasting of Complex Spatiotemporal Dynamics
Backpropagation Algorithms and Reservoir Computing in Recurrent Neural Networks for the Forecasting of Complex Spatiotemporal Dynamics
Pantelis R. Vlachas
Jaideep Pathak
Brian R. Hunt
T. Sapsis
M. Girvan
Edward Ott
Petros Koumoutsakos
AI4TS
61
395
0
09 Oct 2019
Is a Good Representation Sufficient for Sample Efficient Reinforcement
  Learning?
Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning?
S. Du
Sham Kakade
Ruosong Wang
Lin F. Yang
190
193
0
07 Oct 2019
Modeling the Dynamics of PDE Systems with Physics-Constrained Deep
  Auto-Regressive Networks
Modeling the Dynamics of PDE Systems with Physics-Constrained Deep Auto-Regressive Networks
N. Geneva
N. Zabaras
AI4CE
69
275
0
13 Jun 2019
Machine Learning for Fluid Mechanics
Machine Learning for Fluid Mechanics
Steven Brunton
B. R. Noack
Petros Koumoutsakos
AI4CEPINN
87
2,128
0
27 May 2019
Exascale Deep Learning for Climate Analytics
Exascale Deep Learning for Climate Analytics
Thorsten Kurth
Sean Treichler
Josh Romero
M. Mudigonda
Nathan Luehr
...
Michael A. Matheson
J. Deslippe
M. Fatica
P. Prabhat
Michael Houston
BDL
73
263
0
03 Oct 2018
Deep convolutional recurrent autoencoders for learning low-dimensional
  feature dynamics of fluid systems
Deep convolutional recurrent autoencoders for learning low-dimensional feature dynamics of fluid systems
F. J. Gonzalez
Maciej Balajewicz
AI4CE
129
140
0
03 Aug 2018
Latent-space Physics: Towards Learning the Temporal Evolution of Fluid
  Flow
Latent-space Physics: Towards Learning the Temporal Evolution of Fluid Flow
S. Wiewel
M. Becher
N. Thürey
AI4CE
97
276
0
27 Feb 2018
Data-Driven Forecasting of High-Dimensional Chaotic Systems with Long
  Short-Term Memory Networks
Data-Driven Forecasting of High-Dimensional Chaotic Systems with Long Short-Term Memory Networks
Pantelis R. Vlachas
Wonmin Byeon
Z. Y. Wan
T. Sapsis
Petros Koumoutsakos
AI4TS
68
475
0
21 Feb 2018
Efficient collective swimming by harnessing vortices through deep
  reinforcement learning
Efficient collective swimming by harnessing vortices through deep reinforcement learning
S. Verma
G. Novati
Petros Koumoutsakos
61
365
0
07 Feb 2018
Octree Generating Networks: Efficient Convolutional Architectures for
  High-resolution 3D Outputs
Octree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputs
Maxim Tatarchenko
Alexey Dosovitskiy
Thomas Brox
3DV
92
741
0
28 Mar 2017
Simple and Scalable Predictive Uncertainty Estimation using Deep
  Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCVBDL
842
5,841
0
05 Dec 2016
Overcoming catastrophic forgetting in neural networks
Overcoming catastrophic forgetting in neural networks
J. Kirkpatrick
Razvan Pascanu
Neil C. Rabinowitz
J. Veness
Guillaume Desjardins
...
A. Grabska-Barwinska
Demis Hassabis
Claudia Clopath
D. Kumaran
R. Hadsell
CLL
374
7,572
0
02 Dec 2016
OctNet: Learning Deep 3D Representations at High Resolutions
OctNet: Learning Deep 3D Representations at High Resolutions
Gernot Riegler
Ali O. Ulusoy
Andreas Geiger
3DV3DPC
230
1,481
0
15 Nov 2016
Adam: A Method for Stochastic Optimization
Adam: A Method for Stochastic Optimization
Diederik P. Kingma
Jimmy Ba
ODL
2.1K
150,364
0
22 Dec 2014
Explaining and Harnessing Adversarial Examples
Explaining and Harnessing Adversarial Examples
Ian Goodfellow
Jonathon Shlens
Christian Szegedy
AAMLGAN
282
19,129
0
20 Dec 2014
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