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

Journal Article · Sat Aug 15 00:00:00 EDT 2015 · Journal of Computational Physics

DOI:https://doi.org/10.1016/J.JCP.2015.03.071· OSTI ID:22465641

Farrell, Kathryn; Faghihi, Danial

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.

• OSTI ID: 22465641
• Journal Information: Journal of Computational Physics, Vol. 295; Other Information: Copyright (c) 2015 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.; Country of input: International Atomic Energy Agency (IAEA); ISSN 0021-9991
• Country of Publication: United States
• Language: English

Related Subjects

71 CLASSICAL AND QUANTUM MECHANICS
GENERAL PHYSICS
ALGORITHMS
ATOMS
CALIBRATION
PROBABILITY
SIMULATION
STATISTICS
VALIDATION