Hao Wu

发表

Stochastic Normalizing Flows pdf

Hao Wu, Jonas Kohler, Frank No'e, 2020, NeurIPS.

Stochastic Normalizing Flows

Hao Wu, Jonas Köhler, Frank No'e, 2020, NeurIPS.

Statistically optimal analysis of state-discretized trajectory data from multiple thermodynamic states.

Frank Noé, Edina Rosta, Antonia S J S Mey, 2014, The Journal of chemical physics.

On transport of ionic solutions: from kinetic laws to continuum descriptions

Hao Wu, Chun Liu, Tai-Chia Lin, 2013 .

Projected and hidden Markov models for calculating kinetics and metastable states of complex molecules.

F. Noé, Hao Wu, Nuria Plattner, 2013, The Journal of chemical physics.

Protein-peptide association kinetics beyond the seconds timescale from atomistic simulations

Esam T Abualrous, F. Noé, J. Clarke, 2017, Nature Communications.

Aggregate Structure in Heavy Crude Oil: Using a Dissipative Particle Dynamics Based Mesoscale Platform

Hao Wu, Hao Wen, Junbo Xu, 2010 .

Data-Driven Model Reduction and Transfer Operator Approximation

C. Schütte, I. Kevrekidis, F. Noé, 2018, Journal of Nonlinear Science.

Variational Approach for Learning Markov Processes from Time Series Data

F. Noé, Hao Wu, 2019, Journal of Nonlinear Science.

Spectral Learning of Dynamic Systems from Nonequilibrium Data

Hao Wu, Frank Noé, F. Noé, 2016, NIPS.

Deep learning Markov and Koopman models with physical constraints

Hao Wu, Andreas Mardt, Luca Pasquali, 2019, MSML.

Variational Koopman models: Slow collective variables and molecular kinetics from short off-equilibrium simulations.

Frank Noé, Feliks Nüske, Stefan Klus, 2016, The Journal of chemical physics.

Multiensemble Markov models of molecular thermodynamics and kinetics

Hao Wu, Frank Noé, Fabian Paul, 2016, Proceedings of the National Academy of Sciences.

Markov state models from short non-equilibrium simulations—Analysis and correction of estimation bias

Cecilia Clementi, Frank No'e, Feliks Nuske, 2017, 1701.01665.

Estimation and uncertainty of reversible Markov models.

Hao Wu, Frank Noé, Fabian Paul, 2015, The Journal of chemical physics.

Mesoscopic simulation of self-assembly in surfactant oligomers by dissipative particle dynamics

Hao Wu, H. Wen, Yuehong Zhao, 2006 .

Variational Approach for Learning Markov Processes from Time Series Data

Hao Wu, Frank No'e, Hao Wu, 2017, Journal of Nonlinear Science.

Kernel Embedding Based Variational Approach for Low-Dimensional Approximation of Dynamical Systems

Wenchong Tian, Hao Wu, Hao Wu, 2020, Comput. Methods Appl. Math..

Data-Driven Model Reduction and Transfer Operator Approximation

Hao Wu, Frank Noé, Feliks Nüske, 2017, J. Nonlinear Sci..

Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning

Hao Wu, Frank Noé, F. Noé, 2018, Science.

GPU Accelerated Dissipative Particle Dynamics with Parallel Cell-list Updating

Hao Wu, Hao Wen, Junbo Xu, 2011 .

Markov models of molecular kinetics: generation and validation.

Frank Noé, Bettina Keller, Martin Held, 2011, The Journal of chemical physics.

A flat Dirichlet process switching model for Bayesian estimation of hybrid systems

Hao Wu, Frank Noé, F. Noé, 2011, ICCS.

Optimal Data-Driven Estimation of Generalized Markov State Models for Non-Equilibrium Dynamics

Hao Wu, Frank Noé, Christof Schütte, 2018, Comput..

VAMPnets for deep learning of molecular kinetics

Hao Wu, Frank Noé, Andreas Mardt, 2017, Nature Communications.

xTRAM: Estimating equilibrium expectations from time-correlated simulation data at multiple thermodynamic states

Hao Wu, Frank No'e, Antonia S. J. S. Mey, 2014, 1407.0138.

Deep Generative Markov State Models

Hao Wu, Frank Noé, Andreas Mardt, 2018, NeurIPS.

Optimal Estimation of Free Energies and Stationary Densities from Multiple Biased Simulations

Hao Wu, Frank Noé, F. Noé, 2012, Multiscale Model. Simul..

ε-Dependent controllability for two time-scale systems

Fuchun Sun, Huaping Liu, Yenan Hu, 2009 .

Deeptime: a Python library for machine learning dynamical models from time series data

Frank Noé, Stefan Klus, Andreas Mardt, 2021, Mach. Learn. Sci. Technol..

Rapid Calculation of Molecular Kinetics Using Compressed Sensing.

Hao Wu, Frank Noé, Feliks Nüske, 2018, Journal of chemical theory and computation.

Probability Distance Based Compression of Hidden Markov Models

Hao Wu, Frank Noé, F. Noé, 2010, Multiscale Model. Simul..

Bayesian framework for modeling diffusion processes with nonlinear drift based on nonlinear and incomplete observations.

Frank Noé, Hao Wu, F. Noé, 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

Combining experimental and simulation data of molecular processes via augmented Markov models

Hao Wu, Frank Noé, Cecilia Clementi, 2017, Proceedings of the National Academy of Sciences.

Gaussian Markov transition models of molecular kinetics.

Frank Noé, Hao Wu, F. Noé, 2015, The Journal of chemical physics.

A Bayesian Nonparametric Model for Spectral Estimation of Metastable Systems

Hao Wu, Hao Wu, 2014, UAI.

Parameterized Wasserstein Hamiltonian Flow

Haomin Zhou, X. Ye, Hao Wu, 2023, 2306.00191.

Branch-and-Cut Algorithmic Framework for 0-1 Mixed-Integer Convex Nonlinear Programs

Hao Wu, Yushan Zhu, Yushan Zhu, 2009 .

Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning

F. Noé, Hao Wu, Jonas Köhler, 2019, Science.

Stochastic Normalizing Flows

Hao Wu, 2020 .

Projected metastable Markov processes and their estimation with observable operator models.

Hao Wu, Frank Noé, Jan-Hendrik Prinz, 2015, The Journal of chemical physics.

Maximum margin clustering for state decomposition of metastable systems

Hao Wu, Hao Wu, 2013, Neurocomputing.

Maximum a posteriori estimation for Markov chains based on Gaussian Markov random fields

Hao Wu, Frank Noé, F. Noé, 2010, ICCS.

Parameterized Wasserstein Hamiltonian Flow

Haomin Zhou, X. Ye, Hao Wu, 2023, ArXiv.

Author Correction: VAMPnets for deep learning of molecular kinetics

Frank Noé, Andreas Mardt, Luca Pasquali, 2018, Nature Communications.

Reversible Markov chain estimation using convex-concave programming

F. Noé, Hao Wu, Benjamin Trendelkamp-Schroer, 2016, 1603.01640.

State estimation for Markovian Jump Linear Systems with bounded disturbances

Wei Wang, Hao Wu, Hao Ye, 2013, Autom..

Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning

F. Noé, Hao Wu, Jonas Köhler, 2019, Science.

Deep Reinforcement Learning based Applications in Smart Power Systems

Hao Wu, Yong Chen, Xi Peng, 2021, Journal of Physics: Conference Series.