Capabilities and limitations of time-lagged autoencoders for slow mode discovery in dynamical systems
Published 2019 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Capabilities and limitations of time-lagged autoencoders for slow mode discovery in dynamical systems
Authors
Keywords
-
Journal
JOURNAL OF CHEMICAL PHYSICS
Volume 151, Issue 6, Pages 064123
Publisher
AIP Publishing
Online
2019-08-15
DOI
10.1063/1.5112048
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Nonlinear discovery of slow molecular modes using state-free reversible VAMPnets
- (2019) Wei Chen et al. JOURNAL OF CHEMICAL PHYSICS
- Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control
- (2018) Milan Korda et al. AUTOMATICA
- Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics
- (2018) Christoph Wehmeyer et al. JOURNAL OF CHEMICAL PHYSICS
- Transferable Neural Networks for Enhanced Sampling of Protein Dynamics
- (2018) Mohammad M. Sultan et al. Journal of Chemical Theory and Computation
- Data-Driven Model Reduction and Transfer Operator Approximation
- (2018) Stefan Klus et al. JOURNAL OF NONLINEAR SCIENCE
- Markov State Models: From an Art to a Science
- (2018) Brooke E. Husic et al. JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
- Recruiting machine learning methods for molecular simulations of proteins
- (2018) Shriyaa Mittal et al. MOLECULAR SIMULATION
- Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach
- (2018) Jaideep Pathak et al. PHYSICAL REVIEW LETTERS
- VAMPnets for deep learning of molecular kinetics
- (2018) Andreas Mardt et al. Nature Communications
- NSF’s Inaugural Software Institutes: The Science Gateways Community Institute and the Molecular Sciences Software Institute
- (2018) Nancy Wilkins-Diehr et al. COMPUTING IN SCIENCE & ENGINEERING
- Perspective: Computational chemistry software and its advancement as illustrated through three grand challenge cases for molecular science
- (2018) Anna Krylov et al. JOURNAL OF CHEMICAL PHYSICS
- Note: Variational encoding of protein dynamics benefits from maximizing latent autocorrelation
- (2018) Hannah K. Wayment-Steele et al. JOURNAL OF CHEMICAL PHYSICS
- On Convergence of Extended Dynamic Mode Decomposition to the Koopman Operator
- (2017) Milan Korda et al. JOURNAL OF NONLINEAR SCIENCE
- Transfer Learning from Markov Models Leads to Efficient Sampling of Related Systems
- (2017) Mohammad M. Sultan et al. JOURNAL OF PHYSICAL CHEMISTRY B
- OpenMM 7: Rapid development of high performance algorithms for molecular dynamics
- (2017) Peter Eastman et al. PLoS Computational Biology
- Hierarchical Time-Lagged Independent Component Analysis: Computing Slow Modes and Reaction Coordinates for Large Molecular Systems
- (2016) Guillermo Pérez-Hernández et al. Journal of Chemical Theory and Computation
- Commute Maps: Separating Slowly Mixing Molecular Configurations for Kinetic Modeling
- (2016) Frank Noé et al. Journal of Chemical Theory and Computation
- Correspondence between Koopman mode decomposition, resolvent mode decomposition, and invariant solutions of the Navier-Stokes equations
- (2016) Ati S. Sharma et al. Physical Review Fluids
- Estimation and uncertainty of reversible Markov models
- (2015) Benjamin Trendelkamp-Schroer et al. JOURNAL OF CHEMICAL PHYSICS
- Modeling Molecular Kinetics with tICA and the Kernel Trick
- (2015) Christian R. Schwantes et al. Journal of Chemical Theory and Computation
- Kinetic Distance and Kinetic Maps from Molecular Dynamics Simulation
- (2015) Frank Noé et al. Journal of Chemical Theory and Computation
- Equation-free mechanistic ecosystem forecasting using empirical dynamic modeling
- (2015) Hao Ye et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Markov state models of biomolecular conformational dynamics
- (2014) John D Chodera et al. CURRENT OPINION IN STRUCTURAL BIOLOGY
- Variational Approach to Molecular Kinetics
- (2014) Feliks Nüske et al. Journal of Chemical Theory and Computation
- Identification of slow molecular order parameters for Markov model construction
- (2013) Guillermo Pérez-Hernández et al. JOURNAL OF CHEMICAL PHYSICS
- Improvements in Markov State Model Construction Reveal Many Non-Native Interactions in the Folding of NTL9
- (2013) Christian R. Schwantes et al. Journal of Chemical Theory and Computation
- A Variational Approach to Modeling Slow Processes in Stochastic Dynamical Systems
- (2013) Frank Noé et al. MULTISCALE MODELING & SIMULATION
- OpenMM 4: A Reusable, Extensible, Hardware Independent Library for High Performance Molecular Simulation
- (2012) Peter Eastman et al. Journal of Chemical Theory and Computation
- Nonlinear Laplacian spectral analysis for time series with intermittency and low-frequency variability
- (2012) D. Giannakis et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Markov models of molecular kinetics: Generation and validation
- (2011) Jan-Hendrik Prinz et al. JOURNAL OF CHEMICAL PHYSICS
- Everything you wanted to know about Markov State Models but were afraid to ask
- (2010) Vijay S. Pande et al. METHODS
- Improved side-chain torsion potentials for the Amber ff99SB protein force field
- (2010) Kresten Lindorff-Larsen et al. PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
- Stochastic thermostats: comparison of local and global schemes
- (2008) Giovanni Bussi et al. COMPUTER PHYSICS COMMUNICATIONS
Add your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload NowCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now