Variational Encoding of Complex Dynamics

23 November 2017

Papers citing "Variational Encoding of Complex Dynamics"

20 / 20 papers shown

Title
Fast conformational clustering of extensive molecular dynamics simulation data Simon Hunkler K. Diederichs O. Kukharenko Christine Peter 19 9 0 11 Jan 2023
GD-VAEs: Geometric Dynamic Variational Autoencoders for Learning Nonlinear Dynamics and Dimension Reductions Ryan Lopez P. Atzberger AI4CE 34 8 0 10 Jun 2022
GraphVAMPNet, using graph neural networks and variational approach to markov processes for dynamical modeling of biomolecules Mahdi Ghorbani Samarjeet Prasad Jeffery B. Klauda B. Brooks GNN 29 30 0 12 Jan 2022
Collective variable discovery in the age of machine learning: reality, hype and everything in between S. Bhakat AI4CE 34 25 0 06 Dec 2021
Reconstructing Nonlinear Dynamical Systems from Multi-Modal Time Series Daniel Kramer P. Bommer Carlo Tombolini G. Koppe Daniel Durstewitz BDL AI4TS AI4CE 27 19 0 04 Nov 2021
Deeptime: a Python library for machine learning dynamical models from time series data Moritz Hoffmann Martin K. Scherer Tim Hempel Andreas Mardt Brian M. de Silva ... Stefan Klus Hao Wu N. Kutz Steven L. Brunton Frank Noé AI4CE 33 101 0 28 Oct 2021
Coupling streaming AI and HPC ensembles to achieve 100-1000x faster biomolecular simulations Alexander Brace I. Yakushin Heng Ma Anda Trifan T. Munson Ian Foster A. Ramanathan Hyungro Lee Matteo Turilli S. Jha AI4CE 32 19 0 10 Apr 2021
Artificial intelligence techniques for integrative structural biology of intrinsically disordered proteins A. Ramanathan Henglong Ma Akash Parvatikar C. Chennubhotla AI4CE 31 40 0 01 Dec 2020
Ensemble Learning of Coarse-Grained Molecular Dynamics Force Fields with a Kernel Approach Jiang Wang Stefan Chmiela K. Müller Frank Noè C. Clementi 10 46 0 04 May 2020
Differentiable Molecular Simulations for Control and Learning Wujie Wang Simon Axelrod Rafael Gómez-Bombarelli AI4CE 111 49 0 27 Feb 2020
Interpretable Embeddings From Molecular Simulations Using Gaussian Mixture Variational Autoencoders Yasemin Bozkurt Varolgunes T. Bereau J. F. Rudzinski DRL 12 42 0 22 Dec 2019
Machine learning for molecular simulation Frank Noé A. Tkatchenko K. Müller C. Clementi AI4CE 24 642 0 07 Nov 2019
DeepDriveMD: Deep-Learning Driven Adaptive Molecular Simulations for Protein Folding Hyungro Lee Heng Ma Matteo Turilli D. Bhowmik S. Jha A. Ramanathan AI4CE 6 70 0 17 Sep 2019
Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning Peter Y. Lu Samuel Kim Marin Soljacic AI4CE 22 59 0 13 Jul 2019
Capabilities and Limitations of Time-lagged Autoencoders for Slow Mode Discovery in Dynamical Systems Wei Chen Hythem Sidky Andrew L. Ferguson 33 36 0 02 Jun 2019
Machine Learning for Molecular Dynamics on Long Timescales Frank Noé AI4CE 25 32 0 18 Dec 2018
Note: Variational Encoding of Protein Dynamics Benefits from Maximizing Latent Autocorrelation H. Wayment-Steele Vijay S. Pande DRL 8 6 0 17 Mar 2018
Automated design of collective variables using supervised machine learning Mohammad M. Sultan Vijay S. Pande 24 121 0 28 Feb 2018
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics C. Wehmeyer Frank Noé AI4CE BDL 111 356 0 30 Oct 2017
Variational Koopman models: slow collective variables and molecular kinetics from short off-equilibrium simulations Hao Wu Feliks Nuske Fabian Paul Stefan Klus P. Koltai Frank Noé 107 126 0 20 Oct 2016