518 citations · 1.5k across the 21 of their papers we have counts for
22 papers
Parameterized Wasserstein Hamiltonian Flow
Hao Wu, Shu Liu, Xiaojing Ye +1
In this work, we propose a numerical method to compute the Wasserstein Hamiltonian flow (WHF), which is a Hamiltonian system on the probability density manifold. Many well-known PD…
Deeptime: a Python library for machine learning dynamical models from time series data
Moritz Hoffmann, Martin Scherer, Tim Hempel +8
Generation and analysis of time-series data is relevant to many quantitative fields ranging from economics to fluid mechanics. In the physical sciences, structures such as metastab…
A Stochastic Gradient Descent Theorem and the Back-Propagation Algorithm
Hao Wu
We establish a convergence theorem for a certain type of stochastic gradient descent, which leads to a convergent variant of the back-propagation algorithm
Kernel Embedding based Variational Approach for Low-dimensional Approximation of Dynamical Systems
Wenchong Tian, Hao Wu
Transfer operators such as Perron-Frobenius or Koopman operator play a key role in modeling and analysis of complex dynamical systems, which allow linear representations of nonline…
Stochastic Normalizing Flows
Hao Wu, Jonas Köhler, Frank Noé
The sampling of probability distributions specified up to a normalization constant is an important problem in both machine learning and statistical mechanics. While classical stoch…
Deep learning Markov and Koopman models with physical constraints
Andreas Mardt, Luca Pasquali, Frank Noé +1
The long-timescale behavior of complex dynamical systems can be described by linear Markov or Koopman models in a suitable latent space. Recent variational approaches allow the lat…