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Found 3 papers, 3 papers with code
Deep learning Markov and Koopman models with physical constraints
Here we develop theory and methods for deep learning Markov and Koopman models that can bear such physical constraints.
Computational Physics
Deep Generative Markov State Models
We propose a deep generative Markov State Model (DeepGenMSM) learning framework for inference of metastable dynamical systems and prediction of trajectories.
VAMPnets: Deep learning of molecular kinetics
There is an increasing demand for computing the relevant structures, equilibria and long-timescale kinetics of biomolecular processes, such as protein-drug binding, from high-throughput molecular dynamics simulations.
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