1 code implementation • 28 Oct 2021 • Moritz Hoffmann, Martin Scherer, Tim Hempel, Andreas Mardt, Brian de Silva, Brooke E. Husic, Stefan Klus, Hao Wu, Nathan Kutz, Steven L. Brunton, Frank Noé
Generation and analysis of time-series data is relevant to many quantitative fields ranging from economics to fluid mechanics.
1 code implementation • 4 Aug 2021 • Andreas Mardt, Frank Noé
Recent advances in deep learning frameworks have established valuable tools for analyzing the long-timescale behavior of complex systems such as proteins.
1 code implementation • 16 Dec 2019 • Andreas Mardt, Luca Pasquali, Frank Noé, Hao Wu
Here we develop theory and methods for deep learning Markov and Koopman models that can bear such physical constraints.
Computational Physics
2 code implementations • NeurIPS 2018 • Hao Wu, Andreas Mardt, Luca Pasquali, Frank Noe
We propose a deep generative Markov State Model (DeepGenMSM) learning framework for inference of metastable dynamical systems and prediction of trajectories.
1 code implementation • 16 Oct 2017 • Andreas Mardt, Luca Pasquali, Hao Wu, Frank Noé
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-throughput molecular dynamics simulations.