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International Journal for Multiscale Computational Engineering

Published 6 issues per year

ISSN Print: 1543-1649

ISSN Online: 1940-4352

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.4 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.3 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 2.2 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.00034 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.46 SJR: 0.333 SNIP: 0.606 CiteScore™:: 3.1 H-Index: 31

Indexed in

Genetic Programming for Multiscale Modeling

Volume 2, Issue 2, 2004, 19 pages
DOI: 10.1615/IntJMultCompEng.v2.i2.50
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ABSTRACT

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.

CITED BY
  1. Sastry Kumara, Johnson D. D., Goldberg David E., Bellon Pascal, Genetic programming for multitimescale modeling, Physical Review B, 72, 8, 2005. Crossref

  2. Tesler A. B., Lewin D. R., Baltianski S., Tsur Y., Analyzing results of impedance spectroscopy using novel evolutionary programming techniques, Journal of Electroceramics, 24, 4, 2010. Crossref

  3. Johnson Duane D., Evolutionary Algorithms Applied to Electronic-Structure Informatics, in Informatics for Materials Science and Engineering, 2013. Crossref

  4. Pelteret Jean-Paul, Walter Bastian, Steinmann Paul, Application of metaheuristic algorithms to the identification of nonlinear magneto-viscoelastic constitutive parameters, Journal of Magnetism and Magnetic Materials, 464, 2018. Crossref

  5. Sun Sheng, Ouyang Runhai, Zhang Bochao, Zhang Tong-Yi, Data-driven discovery of formulas by symbolic regression, MRS Bulletin, 44, 7, 2019. Crossref

  6. Sastry Kumara, Johnson D. D., Thompson Alexis L., Goldberg David E., Martinez Todd J., Leiding Jeff, Owens Jane, Optimization of Semiempirical Quantum Chemistry Methods via Multiobjective Genetic Algorithms: Accurate Photodynamics for Larger Molecules and Longer Time Scales, Materials and Manufacturing Processes, 22, 5, 2007. Crossref

  7. Kabliman Evgeniya, Kolody Ana Helena, Kommenda Michael, Kronberger Gabriel, Prediction of stress-strain curves for aluminium alloys using symbolic regression, PROCEEDINGS OF THE 22ND INTERNATIONAL ESAFORM CONFERENCE ON MATERIAL FORMING: ESAFORM 2019, 2113, 2019. Crossref

  8. Kronberger Gabriel, Kabliman Evgeniya, Kronsteiner Johannes, Kommenda Michael, Extending a physics-based constitutive model using genetic programming, Applications in Engineering Science, 9, 2022. Crossref

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