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VAMPnets: Deep learning of molecular kinetics
v1v2 (latest)

VAMPnets: Deep learning of molecular kinetics

16 October 2017
Andreas Mardt
Luca Pasquali
Hao Wu
Frank Noé
ArXiv (abs)PDFHTML

Papers citing "VAMPnets: Deep learning of molecular kinetics"

34 / 134 papers shown
Title
Incorporating physical constraints in a deep probabilistic machine
  learning framework for coarse-graining dynamical systems
Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systems
Sebastian Kaltenbach
P. Koutsourelakis
AI4CE
205
35
0
30 Dec 2019
Interpretable Embeddings From Molecular Simulations Using Gaussian
  Mixture Variational Autoencoders
Interpretable Embeddings From Molecular Simulations Using Gaussian Mixture Variational Autoencoders
Yasemin Bozkurt Varolgunes
T. Bereau
J. F. Rudzinski
DRL
58
44
0
22 Dec 2019
Machine learning for protein folding and dynamics
Machine learning for protein folding and dynamics
Frank Noé
Gianni De Fabritiis
C. Clementi
AI4CE
121
138
0
22 Nov 2019
Machine learning for molecular simulation
Machine learning for molecular simulation
Frank Noé
A. Tkatchenko
K. Müller
C. Clementi
AI4CE
90
668
0
07 Nov 2019
Deep Learning Models for Global Coordinate Transformations that
  Linearize PDEs
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
Neural Canonical Transformation with Symplectic Flows
Neural Canonical Transformation with Symplectic Flows
Shuo-Hui Li
Chen Dong
Linfeng Zhang
Lei Wang
DRL
119
28
0
30 Sep 2019
Learning Everywhere: A Taxonomy for the Integration of Machine Learning
  and Simulations
Learning Everywhere: A Taxonomy for the Integration of Machine Learning and Simulations
Geoffrey C. Fox
S. Jha
AI4CE
72
13
0
29 Sep 2019
Data-driven approximation of the Koopman generator: Model reduction,
  system identification, and control
Data-driven approximation of the Koopman generator: Model reduction, system identification, and control
Stefan Klus
Feliks Nuske
Sebastian Peitz
Jan-Hendrik Niemann
C. Clementi
Christof Schütte
118
230
0
23 Sep 2019
DeepDriveMD: Deep-Learning Driven Adaptive Molecular Simulations for
  Protein Folding
DeepDriveMD: Deep-Learning Driven Adaptive Molecular Simulations for Protein Folding
Hyungro Lee
Heng Ma
Matteo Turilli
D. Bhowmik
S. Jha
A. Ramanathan
AI4CE
54
71
0
17 Sep 2019
Tensor-based computation of metastable and coherent sets
Tensor-based computation of metastable and coherent sets
Feliks Nuske
Patrick Gelß
Stefan Klus
C. Clementi
32
13
0
12 Aug 2019
A unified sparse optimization framework to learn parsimonious
  physics-informed models from data
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
74
119
0
25 Jun 2019
High-resolution Markov state models for the dynamics of Trp-cage
  miniprotein constructed over slow folding modes identified by state-free
  reversible VAMPnets
High-resolution Markov state models for the dynamics of Trp-cage miniprotein constructed over slow folding modes identified by state-free reversible VAMPnets
Hythem Sidky
Wei Chen
Andrew L. Ferguson
74
55
0
12 Jun 2019
Capabilities and Limitations of Time-lagged Autoencoders for Slow Mode
  Discovery in Dynamical Systems
Capabilities and Limitations of Time-lagged Autoencoders for Slow Mode Discovery in Dynamical Systems
Wei Chen
Hythem Sidky
Andrew L. Ferguson
76
36
0
02 Jun 2019
Machine Learning for Fluid Mechanics
Machine Learning for Fluid Mechanics
Steven Brunton
B. R. Noack
Petros Koumoutsakos
AI4CEPINN
103
2,146
0
27 May 2019
Structure-preserving Method for Reconstructing Unknown Hamiltonian
  Systems from Trajectory Data
Structure-preserving Method for Reconstructing Unknown Hamiltonian Systems from Trajectory Data
Kailiang Wu
Tong Qin
D. Xiu
78
31
0
24 May 2019
Approximation spaces of deep neural networks
Approximation spaces of deep neural networks
Rémi Gribonval
Gitta Kutyniok
M. Nielsen
Felix Voigtländer
111
126
0
03 May 2019
Dimensionality Reduction of Complex Metastable Systems via Kernel
  Embeddings of Transition Manifolds
Dimensionality Reduction of Complex Metastable Systems via Kernel Embeddings of Transition Manifolds
A. Bittracher
Stefan Klus
B. Hamzi
P. Koltai
Christof Schütte
78
22
0
18 Apr 2019
Kernel methods for detecting coherent structures in dynamical data
Kernel methods for detecting coherent structures in dynamical data
Stefan Klus
B. Husic
Mattes Mollenhauer
Frank Noé
48
29
0
16 Apr 2019
Time Series Source Separation using Dynamic Mode Decomposition
Time Series Source Separation using Dynamic Mode Decomposition
Arvind Prasadan
R. Nadakuditi
AI4TS
88
6
0
04 Mar 2019
Graph Dynamical Networks for Unsupervised Learning of Atomic Scale
  Dynamics in Materials
Graph Dynamical Networks for Unsupervised Learning of Atomic Scale Dynamics in Materials
T. Xie
A. France-Lanord
Yanming Wang
Y. Shao-horn
Jeffrey C. Grossman
AI4CE
69
111
0
18 Feb 2019
Nonlinear Discovery of Slow Molecular Modes using State-Free Reversible
  VAMPnets
Nonlinear Discovery of Slow Molecular Modes using State-Free Reversible VAMPnets
Wei Chen
Hythem Sidky
Andrew L. Ferguson
54
100
0
09 Feb 2019
Machine Learning for Molecular Dynamics on Long Timescales
Machine Learning for Molecular Dynamics on Long Timescales
Frank Noé
AI4CE
77
32
0
18 Dec 2018
Coarse-Graining Auto-Encoders for Molecular Dynamics
Coarse-Graining Auto-Encoders for Molecular Dynamics
Wujie Wang
Rafael Gómez-Bombarelli
AI4CE
70
167
0
06 Dec 2018
Machine Learning of coarse-grained Molecular Dynamics Force Fields
Machine Learning of coarse-grained Molecular Dynamics Force Fields
Jiang Wang
Simon Olsson
C. Wehmeyer
Adria Pérez
Nicholas E. Charron
Gianni De Fabritiis
Frank Noe
C. Clementi
AI4CE
83
407
0
04 Dec 2018
Variational Selection of Features for Molecular Kinetics
Variational Selection of Features for Molecular Kinetics
Martin K. Scherer
B. Husic
Moritz Hoffmann
Fabian Paul
Hao Wu
Frank Noé
117
50
0
28 Nov 2018
Data Driven Governing Equations Approximation Using Deep Neural Networks
Data Driven Governing Equations Approximation Using Deep Neural Networks
Tong Qin
Kailiang Wu
D. Xiu
PINN
98
275
0
13 Nov 2018
A kernel-based approach to molecular conformation analysis
A kernel-based approach to molecular conformation analysis
Stefan Klus
A. Bittracher
Ingmar Schuster
Christof Schütte
57
26
0
28 Sep 2018
Stress Field Prediction in Cantilevered Structures Using Convolutional
  Neural Networks
Stress Field Prediction in Cantilevered Structures Using Convolutional Neural Networks
Zhenguo Nie
Haoliang Jiang
Levent Burak Kara
53
144
0
27 Aug 2018
Deep Generative Markov State Models
Deep Generative Markov State Models
Hao Wu
Andreas Mardt
Luca Pasquali
Frank Noe
AI4CE
71
60
0
19 May 2018
Deep learning for universal linear embeddings of nonlinear dynamics
Deep learning for universal linear embeddings of nonlinear dynamics
Bethany Lusch
J. Nathan Kutz
Steven L. Brunton
87
1,268
0
27 Dec 2017
Variational Encoding of Complex Dynamics
Variational Encoding of Complex Dynamics
Carlos X. Hernández
H. Wayment-Steele
Mohammad M. Sultan
B. Husic
Vijay S. Pande
AI4CE
91
140
0
23 Nov 2017
Time-lagged autoencoders: Deep learning of slow collective variables for
  molecular kinetics
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics
C. Wehmeyer
Frank Noé
AI4CEBDL
179
362
0
30 Oct 2017
Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition
Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition
Naoya Takeishi
Yoshinobu Kawahara
Takehisa Yairi
65
374
0
12 Oct 2017
Variational approach for learning Markov processes from time series data
Variational approach for learning Markov processes from time series data
Hao Wu
Frank Noé
BDLAI4TS
86
266
0
14 Jul 2017
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