250 citations · 963 across the 41 of their papers we have counts for
43 papers
Cluster formation for weakly interacting kinetic Langevin dynamics
Benedict Leimkuhler, René Lohmann, Grigorios A. Pavliotis +1
In this paper, we study the formation of clusters for stochastic interacting particle systems (IPS) that interact through short-range attractive potentials in a periodic domain. We…
How deep is your network? Deep vs. shallow learning of transfer operators
Mohammad Tabish, Benedict Leimkuhler, Stefan Klus
We propose a randomized neural network approach called RaNNDy for learning transfer operators and their spectral decompositions from data. The weights of the hidden layers of the n…
Sampling from Bayesian Neural Network Posteriors with Symmetric Minibatch Splitting Langevin Dynamics
Daniel Paulin, Peter A. Whalley, Neil K. Chada +1
We propose a scalable kinetic Langevin dynamics algorithm for sampling parameter spaces of big data and AI applications. Our scheme combines a symmetric forward/backward sweep over…
Unbiased Kinetic Langevin Monte Carlo with Inexact Gradients
Neil K. Chada, Benedict Leimkuhler, Daniel Paulin +1
We present an unbiased method for Bayesian posterior means based on kinetic Langevin dynamics that combines advanced splitting methods with enhanced gradient approximations. Our ap…
Numerical Methods with Coordinate Transforms for Efficient Brownian Dynamics Simulations
Dominic Phillips, Charles Matthews, Benedict Leimkuhler
Many stochastic processes in the physical and biological sciences can be modelled as Brownian dynamics with multiplicative noise. However, numerical integrators for these processes…
Contraction Rate Estimates of Stochastic Gradient Kinetic Langevin Integrators
Benedict Leimkuhler, Daniel Paulin, Peter A. Whalley
In previous work, we introduced a method for determining convergence rates for integration methods for the kinetic Langevin equation for -Lipschitz -log-concave densi…