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Hao Wu
Hao Wu
Verified email at zedat.fu-berlin.de
Title
Cited by
Cited by
Year
Markov models of molecular kinetics: Generation and validation
JH Prinz, H Wu, M Sarich, B Keller, M Senne, M Held, JD Chodera, ...
The Journal of chemical physics 134 (17), 2011
13342011
Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning
F Noé, S Olsson, J Köhler, H Wu
Science 365 (6457), eaaw1147, 2019
7152019
VAMPnets for deep learning of molecular kinetics
A Mardt, L Pasquali, H Wu, F Noé
Nature communications 9 (1), 5, 2018
6502018
Data-driven model reduction and transfer operator approximation
S Klus, F Nüske, P Koltai, H Wu, I Kevrekidis, C Schütte, F Noé
Journal of Nonlinear Science 28, 985-1010, 2018
3172018
Variational approach for learning Markov processes from time series data
H Wu, F Noé
Journal of Nonlinear Science 30 (1), 23-66, 2020
2942020
Multiensemble Markov models of molecular thermodynamics and kinetics
H Wu, F Paul, C Wehmeyer, F Noé
Proceedings of the National Academy of Sciences 113 (23), E3221-E3230, 2016
2202016
Projected and hidden Markov models for calculating kinetics and metastable states of complex molecules
F Noé, H Wu, JH Prinz, N Plattner
The Journal of chemical physics 139 (18), 2013
1862013
Stochastic normalizing flows
H Wu, J Köhler, F Noé
Advances in Neural Information Processing Systems 33, 5933-5944, 2020
1832020
Estimation and uncertainty of reversible Markov models
B Trendelkamp-Schroer, H Wu, F Paul, F Noé
The Journal of chemical physics 143 (17), 2015
1562015
Variational Koopman models: Slow collective variables and molecular kinetics from short off-equilibrium simulations
H Wu, F Nüske, F Paul, S Klus, P Koltai, F Noé
The Journal of chemical physics 146 (15), 2017
1502017
Protein-peptide association kinetics beyond the seconds timescale from atomistic simulations
F Paul, C Wehmeyer, ET Abualrous, H Wu, MD Crabtree, J Schöneberg, ...
Nature communications 8 (1), 1095, 2017
1482017
Combining experimental and simulation data of molecular processes via augmented Markov models
S Olsson, H Wu, F Paul, C Clementi, F Noé
Proceedings of the National Academy of Sciences 114 (31), 8265-8270, 2017
1132017
Deeptime: a Python library for machine learning dynamical models from time series data
M Hoffmann, M Scherer, T Hempel, A Mardt, B de Silva, BE Husic, S Klus, ...
Machine Learning: Science and Technology 3 (1), 015009, 2021
1072021
Statistically optimal analysis of state-discretized trajectory data from multiple thermodynamic states
H Wu, ASJS Mey, E Rosta, F Noé
The Journal of Chemical Physics 141 (21), 2014
1032014
Markov state models from short non-equilibrium simulations—Analysis and correction of estimation bias
F Nüske, H Wu, JH Prinz, C Wehmeyer, C Clementi, F Noé
The Journal of Chemical Physics 146 (9), 2017
872017
Deep generative markov state models
H Wu, A Mardt, L Pasquali, F Noe
Advances in Neural Information Processing Systems 31, 2018
802018
A variational approach for learning from positive and unlabeled data
H Chen, F Liu, Y Wang, L Zhao, H Wu
Advances in Neural Information Processing Systems 33, 14844-14854, 2020
682020
Variational selection of features for molecular kinetics
MK Scherer, BE Husic, M Hoffmann, F Paul, H Wu, F Noé
The Journal of chemical physics 150 (19), 2019
682019
Monte Carlo fPINNs: Deep learning method for forward and inverse problems involving high dimensional fractional partial differential equations
L Guo, H Wu, X Yu, T Zhou
Computer Methods in Applied Mechanics and Engineering 400, 115523, 2022
652022
xTRAM: Estimating equilibrium expectations from time-correlated simulation data at multiple thermodynamic states
ASJS Mey, H Wu, F Noé
Physical Review X 4 (4), 041018, 2014
642014
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