Quantifying Sampling Noise and Parametric Uncertainty in Atomistic-to-Continuum Simulations Using Surrogate Models

DOI10.1137/140989601zbMath1329.65022OpenAlexW1154724549MaRDI QIDQ3459642

Bert J. Debusschere, Reese E. Jones, Habib N. Najm, Maher Salloum, Khachik V. Sargsyan

Publication date: 11 January 2016

Published in: Multiscale Modeling & Simulation (Search for Journal in Brave)

Full work available at URL: https://www.osti.gov/biblio/1184470

zbMATH Keywords

continuumparametric uncertaintymultiscalepolynomial chaosatomisticsampling noise

Mathematics Subject Classification ID

Point estimation (62F10) Bayesian inference (62F15) Monte Carlo methods (65C05)

Related Items

Stochastic Domain Decomposition via Moment Minimization

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Multiscale methods for composites: A review
• Stochastic spectral methods for efficient Bayesian solution of inverse problems
• Adaptive hierarchical modeling of heterogeneous structures
• Fast parallel algorithms for short-range molecular dynamics
• Hybrid atomistic--continuum method for the simulation of dense fluid flows
• Hybrid atomistic-continuum formulations and the moving contact-line problem
• Analytical and numerical study of coupled atomistic-continuum methods for fluids
• A Hybrid Particle-Continuum Method for Hydrodynamics of Complex Fluids
• On the convergence of generalized polynomial chaos expansions
• A nonintrusive stochastic multiscale solver
• A Stochastic Multiscale Coupling Scheme to Account for Sampling Noise in Atomistic-to-Continuum Simulations
• Dimension reduction in stochastic modeling of coupled problems
• Measure transformation and efficient quadrature in reduced-dimensional stochastic modeling of coupled problems
• Multiscale modelling and computational strategies for composites
• Uncertainty Quantification for Atomistic Reaction Models: An Equation-Free Stochastic Simulation Algorithm Example
• Protein labeling reactions in electrochemical microchannel flow: Numerical simulation and uncertainty propagation
• Uncertainty Quantification and Polynomial Chaos Techniques in Computational Fluid Dynamics
• Spectral Methods for Uncertainty Quantification
• Numerical Challenges in the Use of Polynomial Chaos Representations for Stochastic Processes
• Uncertainty Quantification in MD Simulations. Part I: Forward Propagation
• Uncertainty Quantification in MD Simulations. Part II: Bayesian Inference of Force-Field Parameters
• A model for hybrid simulations of molecular dynamics and computational fluid dynamics