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Found 5 papers, 1 papers with code
Generative methods for sampling transition paths in molecular dynamics
Molecular systems often remain trapped for long times around some local minimum of the potential energy function, before switching to another one -- a behavior known as metastability.
Quantifying the mini-batching error in Bayesian inference for Adaptive Langevin dynamics
Bayesian inference allows to obtain useful information on the parameters of models, either in computational statistics or more recently in the context of Bayesian Neural Networks.
Chasing Collective Variables using Autoencoders and biased trajectories
In this context, approaches where the CVs are learned in an iterative way using adaptive biasing have been proposed: at each iteration, the learned CV is used to perform free energy adaptive biasing to generate new data and learn a new CV.
Hybrid Monte Carlo methods for sampling probability measures on submanifolds
In order to avoid biases in the invariant probability measures sampled by discretizations of these stochastically perturbed Hamiltonian dynamics, a Metropolis rejection procedure can be considered.
Numerical Analysis Numerical Analysis 65P10, 65C40, 82-08
Robust determination of maximally-localized Wannier functions
We propose an algorithm to determine Maximally Localized Wannier Functions (MLWFs).
Materials Science Mesoscale and Nanoscale Physics Mathematical Physics Mathematical Physics
