Free energy computations by minimization of Kullback-Leibler divergence: An efficient adaptive biasing potential method for sparse representations
type: Item
instance of: scholarly article
title: Free energy computations by minimization of Kullback-Leibler divergence: An efficient adaptive biasing potential method for sparse representations
zbMATH Open document ID: 1252.82051
author: I. Bilionis
author: Phaedon-Stelios Koutsourelakis
published in: Journal of Computational Physics
publication date: 2012-05-14T00:00:00Z
full work available at URL: https://arxiv.org/abs/1011.2416
review text: The present paper proposes an adaptive biasing potential technique for the computation of free energy landscapes, providing a unifying framework for estimating the free energy function simultaneously with biasing the dynamics. The minimization of the Kullback-Leibler divergence in the extended space provides rigorous convergence bounds and diagnostics. It employs parallelizable sampling schemes that are based on the adaptive sequential Monte Carlo method. The sequential nature of the learning and sampling scheme enables the efficient calculation of free energy functions parametrized by the temperature. The characteristics and capabilities of the proposed method are demonstrated on three numerical examples.
Mathematics Subject Classification ID: 82B30
Mathematics Subject Classification ID: 62M09
Mathematics Subject Classification ID: 65C60
Mathematics Subject Classification ID: 82B80
Mathematics Subject Classification ID: 68T05
zbMATH DE Number: 6034775
zbMATH Keywords: free energy computation
zbMATH Keywords: adaptive biasing potential
zbMATH Keywords: sequential Monte Carlo method
zbMATH Keywords: atomistic simulation
zbMATH Keywords: statistical learning
reviewed by: Dan Polisevski
describes a project that uses: Matlab
MaRDI profile type: Publication
OpenAlex ID: W2962821901
arXiv ID: 1011.2416
cites work: Stability of Stochastic Approximation under Verifiable Conditions
cites work: Q3405563
cites work: Q4836494
cites work: Theoretical and numerical comparison of some sampling methods for molecular dynamics
cites work: Inference in hidden Markov models.
cites work: Central limit theorem for sequential Monte Carlo methods and its application to Bayesian inference
cites work: Q4023085
cites work: Q4819702
cites work: Q3144417
cites work: Sequential Monte Carlo Samplers
cites work: On adaptive resampling strategies for sequential Monte Carlo methods
cites work: Q4139463
cites work: Simulating normalizing constants: From importance sampling to bridge sampling to path sampling
cites work: Interacting sequential Monte Carlo samplers for trans-dimensional simulation
cites work: Statistical Mechanics of Fluid Mixtures
cites work: Design of complex systems in the presence of large uncertainties: a statistical approach
cites work: Accurate Uncertainty Quantification Using Inaccurate Computational Models
cites work: Q5529067
cites work: Recursive Monte Carlo filters: algorithms and theoretical analysis
cites work: Computation of free energy differences through nonequilibrium stochastic dynamics: The reaction coordinate case
cites work: Long-time convergence of an adaptive biasing force method
cites work: Q3560913
cites work: A Generalized Wang–Landau Algorithm for Monte Carlo Computation
cites work: Stochastic Approximation in Monte Carlo Computation
cites work: Advanced Markov Chain Monte Carlo Methods
cites work: Monte Carlo strategies in scientific computing
cites work: Analysis of recursive stochastic algorithms
cites work: The Kolmogorov-Smirnov Test for Goodness of Fit
cites work: Maximum likelihood estimation via the ECM algorithm: A general framework
cites work: Applications of a Kushner and Clark lemma to general classes of stochastic algorithms
cites work: Q4217799
cites work: A Simplex Method for Function Minimization
cites work: A Stochastic Approximation Method
cites work: Q4828566
cites work: Optimal scaling for various Metropolis-Hastings algorithms.
cites work: Q4706739
cites work: Extension to the weighted histogram analysis method: combining umbrella sampling with free energy calculations
cites work: Introduction to Stochastic Search and Optimization
cites work: Path sampling with stochastic dynamics: some new algorithms
cites work: 10.1162/15324430152748236
cites work: Introduction to nonparametric estimation
DOI: 10.1016/J.JCP.2012.01.033
Recommended article: Boltzmann Machine and Mean-Field Approximation for Structured Sparse Decompositions
Recommended article: Free energy methods for Bayesian inference: efficient exploration of univariate Gaussian mixture posteriors
Recommended article: Minimax formula for the replica symmetric free energy of deep restricted Boltzmann machines
Recommended article: Generalised free energy and active inference
Recommended article: Learning and Inference in Sparse Coding Models With Langevin Dynamics
Recommended article: On a computationally scalable sparse formulation of the multidimensional and nonstationary maximum entropy principle
Recommended article: Bias Reduction and Metric Learning for Nearest-Neighbor Estimation of Kullback-Leibler Divergence
Recommended article: An alternative view of variational Bayes and asymptotic approximations of free energy
description: scientific article; zbMATH DE number 6034775