A Bayesian framework for adaptive selection, calibration, and validation of coarse-grained models of atomistic systems

Kathryn Farrell; J. Tinsley Oden; Danial Faghihi

doi:10.1016/j.jcp.2015.03.071

A Bayesian framework for adaptive selection, calibration, and validation of coarse-grained models of atomistic systems

Kathryn Farrell
, J. Tinsley Oden
, Danial Faghihi

Department of Mechanical and Aerospace Engineering

University at Buffalo

University of Texas at Austin

Research output: Contribution to journal › Article › peer-review

78 Scopus citations

Abstract

A general adaptive modeling algorithm for selection and validation of coarse-grained models of atomistic systems is presented. A Bayesian framework is developed to address uncertainties in parameters, data, and model selection. Algorithms for computing output sensitivities to parameter variances, model evidence and posterior model plausibilities for given data, and for computing what are referred to as Occam Categories in reference to a rough measure of model simplicity, make up components of the overall approach. Computational results are provided for representative applications.

Original language	English
Pages (from-to)	189-208
Number of pages	20
Journal	Journal of Computational Physics
Volume	295
DOIs	https://doi.org/10.1016/j.jcp.2015.03.071
State	Published - Aug 5 2015

Keywords

Bayesian inference
Coarse graining models
Model plausibility
Model validation
Output sensitivities

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10.1016/j.jcp.2015.03.071

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abstract = "A general adaptive modeling algorithm for selection and validation of coarse-grained models of atomistic systems is presented. A Bayesian framework is developed to address uncertainties in parameters, data, and model selection. Algorithms for computing output sensitivities to parameter variances, model evidence and posterior model plausibilities for given data, and for computing what are referred to as Occam Categories in reference to a rough measure of model simplicity, make up components of the overall approach. Computational results are provided for representative applications.",

keywords = "Bayesian inference, Coarse graining models, Model plausibility, Model validation, Output sensitivities",

author = "Kathryn Farrell and Oden, \{J. Tinsley\} and Danial Faghihi",

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year = "2015",

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doi = "10.1016/j.jcp.2015.03.071",

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AU - Oden, J. Tinsley

AU - Faghihi, Danial

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Y1 - 2015/8/5

N2 - A general adaptive modeling algorithm for selection and validation of coarse-grained models of atomistic systems is presented. A Bayesian framework is developed to address uncertainties in parameters, data, and model selection. Algorithms for computing output sensitivities to parameter variances, model evidence and posterior model plausibilities for given data, and for computing what are referred to as Occam Categories in reference to a rough measure of model simplicity, make up components of the overall approach. Computational results are provided for representative applications.

AB - A general adaptive modeling algorithm for selection and validation of coarse-grained models of atomistic systems is presented. A Bayesian framework is developed to address uncertainties in parameters, data, and model selection. Algorithms for computing output sensitivities to parameter variances, model evidence and posterior model plausibilities for given data, and for computing what are referred to as Occam Categories in reference to a rough measure of model simplicity, make up components of the overall approach. Computational results are provided for representative applications.

KW - Bayesian inference

KW - Coarse graining models

KW - Model plausibility

KW - Model validation

KW - Output sensitivities

UR - https://www.scopus.com/pages/publications/84928680009

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DO - 10.1016/j.jcp.2015.03.071

M3 - Article

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SP - 189

EP - 208

JO - Journal of Computational Physics

JF - Journal of Computational Physics

ER -

A Bayesian framework for adaptive selection, calibration, and validation of coarse-grained models of atomistic systems

Abstract

Keywords

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