518 citations · 1.5k across the 21 of their papers we have counts for
6 papers · 1 filter
Stochastic Normalizing Flows
Hao Wu, Jonas Köhler, Frank Noé
The sampling of probability distributions specified up to a normalization constant is an important problem in both machine learning and statistical mechanics. While classical stoch…
Boltzmann Generators -- Sampling Equilibrium States of Many-Body Systems with Deep Learning
Frank Noé, Simon Olsson, Jonas Köhler +1
Computing equilibrium states in condensed-matter many-body systems, such as solvated proteins, is a long-standing challenge. Lacking methods for generating statistically independen…
Deep Generative Markov State Models
Hao Wu, Andreas Mardt, Luca Pasquali +1
We propose a deep generative Markov State Model (DeepGenMSM) learning framework for inference of metastable dynamical systems and prediction of trajectories. After unsupervised tra…
VAMPnets: Deep learning of molecular kinetics
Andreas Mardt, Luca Pasquali, Hao Wu +1
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-throu…
Variational approach for learning Markov processes from time series data
Hao Wu, Frank Noé
Inference, prediction and control of complex dynamical systems from time series is important in many areas, including financial markets, power grid management, climate and weather…
Variational Koopman models: slow collective variables and molecular kinetics from short off-equilibrium simulations
Hao Wu, Feliks Nüske, Fabian Paul +3
Markov state models (MSMs) and Master equation models are popular approaches to approximate molecular kinetics, equilibria, metastable states, and reaction coordinates in terms of…