3 citations · 3 across the 2 of their papers we have counts for
9 papers
Nuclear Quantum Effects as a Denoising Problem
Weizhou Wang, Jonathan Weare, Aaron R. Dinner
Nuclear quantum effects are rigorously captured by imaginary-time path integrals, which map the quantum Boltzmann distribution onto a ring polymer of classical replicas. Yet the nu…
AI-boosted rare event sampling to characterize extreme weather
Amaury Lancelin, Alex Wikner, Laurent Dubus +5
Weather extremes pose major societal risks, especially in a changing climate, but due to their rarity, they are difficult to study using limited observations or complex climate mod…
Quantum statistics from classical simulations via generative Gibbs sampling
Weizhou Wang, Xuanxi Zhang, Jonathan Weare +1
Accurate simulation of nuclear quantum effects is essential for molecular modeling but expensive using path integral molecular dynamics (PIMD). We present GG-PI, a ring-polymer-bas…
An exact multiple-time-step variational formulation for the committor and the transition rate
Chatipat Lorpaiboon, Jonathan Weare, Aaron R. Dinner
For a transition between two stable states, the committor is the probability that the dynamics leads to one stable state before the other. It can be estimated from trajectory data…
Sampling parameters of ordinary differential equations with Langevin dynamics that satisfy constraints
Chris Chi, Jonathan Weare, Aaron R. Dinner
Fitting models to data to obtain distributions of consistent parameter values is important for uncertainty quantification, model comparison, and prediction. Standard Markov chain M…
Improved energies and wave function accuracy with Weighted Variational Monte Carlo
Huan Zhang, Robert J. Webber, Michael Lindsey +2
Neural network parametrizations have increasingly been used to represent the ground and excited states in variational Monte Carlo (VMC) with promising results. However, traditional…