Predictive coarse-graining
From MaRDI portal
Publication:1685164
DOI10.1016/j.jcp.2016.10.073zbMath1375.82044arXiv1605.08301OpenAlexW2406299872MaRDI QIDQ1685164
Phaedon-Stelios Koutsourelakis, Nicholas Zabaras, Markus Schöberl
Publication date: 13 December 2017
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1605.08301
Inference from stochastic processes and prediction (62M20) Lattice systems (Ising, dimer, Potts, etc.) and systems on graphs arising in equilibrium statistical mechanics (82B20) Stochastic methods applied to problems in equilibrium statistical mechanics (82B31)
Related Items
Data-driven discovery of coarse-grained equations ⋮ Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systems ⋮ B-spline tight frame based force matching method ⋮ Special issue: Predictive multiscale materials modeling ⋮ A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the small data regime ⋮ Physics-Constrained, Data-Driven Discovery of Coarse-Grained Dynamics
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- A Bayesian framework for adaptive selection, calibration, and validation of coarse-grained models of atomistic systems
- Free energy computations by minimization of Kullback-Leibler divergence: An efficient adaptive biasing potential method for sparse representations
- Multilevel coarse graining and nano-pattern discovery in many particle stochastic systems
- Numerical and statistical methods for the coarse-graining of many-particle stochastic systems
- Uncertainty propagation using infinite mixture of gaussian processes and variational Bayesian inference
- Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems
- Fast parallel algorithms for short-range molecular dynamics
- Convergence of the Monte Carlo expectation maximization for curved exponential families.
- Coarse-grained stochastic processes and Monte Carlo simulations in lattice systems.
- Inference in hidden Markov models.
- Monte Carlo strategies in scientific computing.
- Information loss in coarse-graining of stochastic particle dynamics
- Calibration and validation of coarse-grained models of atomic systems: application to semiconductor manufacturing
- Convergence of Markovian Stochastic Approximation with Discontinuous Dynamics
- From Mesoscale Back to Microscale: Reconstruction Schemes for Coarse-Grained Stochastic Lattice Systems
- A double Metropolis–Hastings sampler for spatial models with intractable normalizing constants
- On the Use of Stochastic Hessian Information in Optimization Methods for Machine Learning
- Multibody Interactions in Coarse-Graining Schemes for Extended Systems
- Graphical Models, Exponential Families, and Variational Inference
- On the convergence of markovian stochastic algorithms with rapidly decreasing ergodicity rates
- Maximizing Generalized Linear Mixed Model Likelihoods With an Automated Monte Carlo EM Algorithm
- Introduction to Stochastic Search and Optimization
- An automated (Markov chain) Monte Carlo EM algorithm
- Stability of Stochastic Approximation under Verifiable Conditions
- Coupling all-atom molecular dynamics simulations of ions in water with Brownian dynamics
- Error Analysis of Coarse‐Graining for Stochastic Lattice Dynamics
- Theoretical and numerical comparison of some sampling methods for molecular dynamics
- A Stochastic Approximation Method
