AI Chat AI Image Generator AI Video Text to Speech

Choice of Damping Coefficient in Langevin Dynamics

06/22/2021
∙
by   Robert D. Skeel, et al.
∙
0
∙

This article considers the application of Langevin dynamics to sampling and investigates how to choose the damping parameter in Langevin dynamics for the purpose of maximizing thoroughness of sampling. Also, it considers the computation of measures of sampling thoroughness.

READ FULL TEXT

page 1

page 2

page 3

page 4

Related Research

research
∙ 09/24/2020

Optimal Sampling Regimes for Estimating Population Dynamics

Ecologists are interested in modeling the population growth of species i...
0 Rebecca E. Bergee, et al. ∙
research
∙ 06/25/2020

Prediction with Gaussian Process Dynamical Models

The modeling and simulation of dynamical systems is a necessary step for...
0 Thomas Beckers, et al. ∙
research
∙ 05/04/2022

Sampling from the ferromagnetic Ising model with external fields

We study the sampling problem for ferromagnetic Ising models with consis...
0 Weiming Feng, et al. ∙
research
∙ 11/30/2021

Optimal friction matrix for underdamped Langevin sampling

A systematic procedure for optimising the friction coefficient in underd...
0 Martin Chak, et al. ∙
research
∙ 07/24/2023

Optimality of Glauber dynamics for general-purpose Ising model sampling and free energy approximation

Recently, Eldan, Koehler, and Zeitouni (2020) showed that Glauber dynami...
0 Dmitriy Kunisky, et al. ∙
research
∙ 06/05/2022

Efficient Frozen Gaussian Sampling Algorithms for Nonadiabatic Quantum Dynamics at Metal Surfaces

In this article, we propose a Frozen Gaussian Sampling (FGS) algorithm f...
0 Zhen Huang, et al. ∙
research
∙ 04/08/2021

On tuning consistent annealed sampling for denoising score matching

Score-based generative models provide state-of-the-art quality for image...
0 Joan Serrà, et al. ∙

Please sign up or login with your details

Forgot password? Click here to reset

Out of credits

Refill your membership to continue using DeepAI

Stripe
Paypal