吴昊｜清华大学｜学者主页

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, B. Husic, Stefan Klus, Hao Wu, N. Kutz, S. Brunton, Frank Noé

Machine Learning: Science and Technology

DOI

2020

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

Wenchong Tian, Hao Wu

Comput. Methods Appl. Math.

DOI

2020

Stochastic Normalizing Flows

Hao Wu, Jonas Köhler, Frank No'e

Neural Information Processing Systems

2019

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

Frank Noé, Jonas Köhler, Hao Wu

Science

DOI

2019

Deep learning Markov and Koopman models with physical constraints

Andreas Mardt, Luca Pasquali, Frank No'e, Hao Wu

Mathematical and Scientific Machine Learning

2018

Rapid Calculation of Molecular Kinetics Using Compressed Sensing.

F. Litzinger, Lorenzo Boninsegna, Hao Wu, F. Nüske, Raajen Patel, Richard Baraniuk, Frank Noé, Cecilia Clementi

Journal of Chemical Theory and Computation

DOI

2018

Deep Generative Markov State Models

Hao Wu, Andreas Mardt, Luca Pasquali, Frank Noé

Neural Information Processing Systems

2018

Data-Driven Model Reduction and Transfer Operator Approximation

Stefan Klus, F. Nüske, P. Koltai, Hao Wu, I. Kevrekidis, C. Schütte, Frank Noé

Journal of nonlinear science

DOI

2018

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

Fabian Paul, C. Wehmeyer, Esam T. Abualrous, Hao Wu, Michael D. Crabtree, Johannes Schöneberg, J. Clarke, C. Freund, T. Weikl, Frank Noé

Nature Communications

DOI

2018

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

P. Koltai, Hao Wu, Frank Noé, C. Schütte

De Computis

DOI

2018

Author Correction: VAMPnets for deep learning of molecular kinetics

Andreas Mardt, Luca Pasquali, Hao Wu, Frank Noé

Nature Communications

DOI

2017

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

S. Olsson, Hao Wu, Fabian Paul, Cecilia Clementi, Frank Noé

Proceedings of the National Academy of Sciences of the United States of America

DOI

2017

VAMPnets for deep learning of molecular kinetics

Andreas Mardt, Luca Pasquali, Hao Wu, Frank Noé

Nature Communications

DOI

2017

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

Fabian Paul, C. Wehmeyer, Esam T. Abualrous, Hao Wu, Michael D. Crabtree, Johannes Schöneberg, J. Clarke, C. Freund, T. Weikl, Frank Noé

Nature Communications

DOI

2017

Variational Approach for Learning Markov Processes from Time Series Data

Hao Wu, Frank No'e

Journal of nonlinear science

DOI

2017

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

F. Nüske, Hao Wu, Jan-Hendrik Prinz, C. Wehmeyer, Cecilia Clementi, Frank No'e

DOI

2016

Spectral Learning of Dynamic Systems from Nonequilibrium Data

Hao Wu, Frank Noé

Neural Information Processing Systems

2016

Reversible Markov chain estimation using convex-concave programming

Benjamin Trendelkamp-Schroer, Hao Wu, Frank Noé

2016

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

Hao Wu, F. Nüske, Fabian Paul, Stefan Klus, P. Koltai, Frank Noé

Journal of Chemical Physics

DOI

2015

Estimation and uncertainty of reversible Markov models.

Benjamin Trendelkamp-Schroer, Hao Wu, Fabian Paul, Frank Noé

Journal of Chemical Physics

DOI

2015

Gaussian Markov transition models of molecular kinetics.

Hao Wu, Frank Noé

Journal of Chemical Physics

DOI

2015

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

Hao Wu, Jan-Hendrik Prinz, Frank Noé

Journal of Chemical Physics

DOI

2014

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

Hao Wu, A. Mey, E. Rosta, Frank Noé

Journal of Chemical Physics

DOI

2014

A Bayesian Nonparametric Model for Spectral Estimation of Metastable Systems

Hao Wu

Conference on Uncertainty in Artificial Intelligence

2014

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

A. Mey, Hao Wu, Frank No'e

DOI

2013

Maximum margin clustering for state decomposition of metastable systems

Hao Wu

Neurocomputing

DOI

2013

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

Frank Noé, Hao Wu, Jan-Hendrik Prinz, Nuria Plattner

Journal of Chemical Physics

DOI

2012

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

Hao Wu, Frank Noé

Multiscale Modeling & simulation

DOI

2011

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

Hao Wu, Frank Noé

Physical review. E, Statistical, nonlinear, and soft matter physics

DOI

2011

Markov models of molecular kinetics: generation and validation.

Jan-Hendrik Prinz, Hao Wu, M. Sarich, B. Keller, Martin Senne, M. Held, J. Chodera, C. Schütte, Frank Noé

Journal of Chemical Physics

DOI

2011

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

Hao Wu, Frank Noé

International Conference on Conceptual Structures

DOI

2010

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

Hao Wu, Frank Noé

International Conference on Conceptual Structures

DOI

2010

Probability Distance Based Compression of Hidden Markov Models

Hao Wu, Frank Noé

Multiscale Modeling & simulation

DOI

吴昊

精选作品

期刊论文

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

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

Stochastic Normalizing Flows

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

Deep learning Markov and Koopman models with physical constraints

Rapid Calculation of Molecular Kinetics Using Compressed Sensing.

Deep Generative Markov State Models

Data-Driven Model Reduction and Transfer Operator Approximation

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

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

Author Correction: VAMPnets for deep learning of molecular kinetics

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

VAMPnets for deep learning of molecular kinetics

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

Variational Approach for Learning Markov Processes from Time Series Data

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

Spectral Learning of Dynamic Systems from Nonequilibrium Data

Reversible Markov chain estimation using convex-concave programming

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

Estimation and uncertainty of reversible Markov models.

Gaussian Markov transition models of molecular kinetics.

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

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

A Bayesian Nonparametric Model for Spectral Estimation of Metastable Systems

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

Maximum margin clustering for state decomposition of metastable systems

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

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

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

Markov models of molecular kinetics: generation and validation.

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

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

Probability Distance Based Compression of Hidden Markov Models

研究方向