Доступ предоставлен для: Guest
Портал Begell Электронная Бибилиотека e-Книги Журналы Справочники и Сборники статей Коллекции
International Journal for Multiscale Computational Engineering
Импакт фактор: 1.016 5-летний Импакт фактор: 1.194 SJR: 0.554 SNIP: 0.68 CiteScore™: 1.18

ISSN Печать: 1543-1649
ISSN Онлайн: 1940-4352

Выпуски:
Том 17, 2019 Том 16, 2018 Том 15, 2017 Том 14, 2016 Том 13, 2015 Том 12, 2014 Том 11, 2013 Том 10, 2012 Том 9, 2011 Том 8, 2010 Том 7, 2009 Том 6, 2008 Том 5, 2007 Том 4, 2006 Том 3, 2005 Том 2, 2004 Том 1, 2003

International Journal for Multiscale Computational Engineering

DOI: 10.1615/IntJMultCompEng.v2.i2.50
19 pages

Genetic Programming for Multiscale Modeling

Kumara Sastry
Department of Material Science & Engineering, Fredrick Seitz Materials Research Laboratory, University of Illinois at Urbana Champaign, Urbana IL 61801
D. D. Johnson
Department of Material Science & Engineering, Fredrick Seitz Materials Research Laboratory, University of Illinois at Urbana Champaign, Urbana IL 61801
David E. Goldberg
Department of General Engineering, University of Illinois at Urbana Champaign, Urbana IL 61801
Pascal Bellon
Department of Material Science & Engineering, Fredrick Seitz Materials Research Laboratory, University of Illinois at Urbana Champaign, Urbana IL 61801

Краткое описание

We propose the use of genetic programming (GP)—a genetic algorithm that evolves computer programs—for bridging simulation methods across multiple scales of time and/or length. The effectiveness of genetic programming in multiscale simulation is demonstrated using two illustrative, non-trivial case studies in science and engineering. The first case is multi-timescale materials kinetics modeling, where genetic programming is used to symbolically regress a mapping of all diffusion barriers from only a few calculated ones, thereby avoiding explicit calculation of all the barriers. The GP-regressed barrier function enables use of kinetic Monte Carlo for realistic potentials and simulation of realistic experimental times (seconds). Specifically, a GP regression is applied to vacancy-assisted migration on a surface of a binary alloy and predict the diffusion barriers within 0.1-1% error using 3% (or less) of the barriers. The second case is the development of constitutive relation between macroscopic variables using measured data, where GP is used to evolve both the function form of the constitutive equation as well as the coefficient values. Specifically, GP regression is used for developing a constitutive relation between flow stress and temperature-compensated strain rate based on microstructural characterization for an aluminum alloy AA7055. We not only reproduce a constitutive relation proposed in literature, but also develop a new constitutive equation that fits both low-strain-rate and high-strain-rate data. We hope these disparate example applications exemplify the power of GP for multiscaling at the price, of course, of not knowing physical details at the intermediate scales.


Articles with similar content:

THE METHOD OF FAILURE PATHS FOR REDUCED-ORDER COMPUTATIONAL HOMOGENIZATION
International Journal for Multiscale Computational Engineering, Vol.14, 2016, issue 5
Caglar Oskay, Paul Sparks
MULTISCALE PARAMETER IDENTIFICATION
International Journal for Multiscale Computational Engineering, Vol.10, 2012, issue 4
Paul Steinmann, Julia Mergheim, Ulrike Schmidt
ASSIMILATION OF COARSE-SCALEDATAUSINGTHE ENSEMBLE KALMAN FILTER
International Journal for Uncertainty Quantification, Vol.1, 2011, issue 1
Yalchin Efendiev, A. Datta-Gupta, Santha Akella
CONSTITUTIVE MODELING FOR PLASTICITY OF METAL POWDERS DURING COMPACTION
Journal of Porous Media, Vol.13, 2010, issue 9
Gholamreza Aryanpour, Masoud Farzaneh
Size of a Representative Volume Element in a Second-Order Computational Homogenization Framework
International Journal for Multiscale Computational Engineering, Vol.2, 2004, issue 4
Marc Geers, W. A. M. Brekelmans, Varvara G. Kouznetsova