| 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 | 1247 | 2011 |
| 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 | 602 | 2019 |
| VAMPnets for deep learning of molecular kinetics A Mardt, L Pasquali, H Wu, F Noé Nature communications 9 (1), 5, 2018 | 579 | 2018 |
| 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 | 287 | 2018 |
| Variational approach for learning Markov processes from time series data H Wu, F Noé Journal of Nonlinear Science 30 (1), 23-66, 2020 | 242 | 2020 |
| 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 | 208 | 2016 |
| 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 | 179 | 2013 |
| Stochastic normalizing flows H Wu, J Köhler, F Noé Advances in Neural Information Processing Systems 33, 5933-5944, 2020 | 147 | 2020 |
| 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 | 142 | 2017 |
| Estimation and uncertainty of reversible Markov models B Trendelkamp-Schroer, H Wu, F Paul, F Noé The Journal of chemical physics 143 (17), 2015 | 141 | 2015 |
| 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 | 134 | 2017 |
| 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 | 107 | 2017 |
| 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 | 100 | 2014 |
| 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 | 79 | 2017 |
| 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 | 74 | 2021 |
| Deep generative markov state models H Wu, A Mardt, L Pasquali, F Noe Advances in Neural Information Processing Systems 31, 2018 | 73 | 2018 |
| 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 | 61 | 2014 |
| 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 | 60 | 2019 |
| 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 | 53 | 2020 |
| 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 | 41 | 2022 |
Noe FrankMicrosoft ResearchVerified email at fu-berlin.de
