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Trilateration-Based Multilevel Method for Minimizing the Lennard-Jones Potential
Simulating atomic evolution for the mechanics and structure of materials presents an ever-growing challenge due to the huge number of degrees of freedom borne from the high-dimensional spaces in which increasingly high-fidelity material models are defined. To efficiently exploit the domain-, data-,...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7302545/ http://dx.doi.org/10.1007/978-3-030-50426-7_13 |
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author | George, Jithin Di, Zichao (Wendy) |
author_facet | George, Jithin Di, Zichao (Wendy) |
author_sort | George, Jithin |
collection | PubMed |
description | Simulating atomic evolution for the mechanics and structure of materials presents an ever-growing challenge due to the huge number of degrees of freedom borne from the high-dimensional spaces in which increasingly high-fidelity material models are defined. To efficiently exploit the domain-, data-, and approximation-based hierarchies hidden in many such problems, we propose a trilateration-based multilevel method to initialize the underlying optimization and benchmark its application on the simple yet practical Lennard-Jones potential. We show that by taking advantage of a known hierarchy present in this problem, not only a faster convergence, but also a better local minimum can be achieved comparing to random initial guess. |
format | Online Article Text |
id | pubmed-7302545 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-73025452020-06-19 Trilateration-Based Multilevel Method for Minimizing the Lennard-Jones Potential George, Jithin Di, Zichao (Wendy) Computational Science – ICCS 2020 Article Simulating atomic evolution for the mechanics and structure of materials presents an ever-growing challenge due to the huge number of degrees of freedom borne from the high-dimensional spaces in which increasingly high-fidelity material models are defined. To efficiently exploit the domain-, data-, and approximation-based hierarchies hidden in many such problems, we propose a trilateration-based multilevel method to initialize the underlying optimization and benchmark its application on the simple yet practical Lennard-Jones potential. We show that by taking advantage of a known hierarchy present in this problem, not only a faster convergence, but also a better local minimum can be achieved comparing to random initial guess. 2020-05-25 /pmc/articles/PMC7302545/ http://dx.doi.org/10.1007/978-3-030-50426-7_13 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article George, Jithin Di, Zichao (Wendy) Trilateration-Based Multilevel Method for Minimizing the Lennard-Jones Potential |
title | Trilateration-Based Multilevel Method for Minimizing the Lennard-Jones Potential |
title_full | Trilateration-Based Multilevel Method for Minimizing the Lennard-Jones Potential |
title_fullStr | Trilateration-Based Multilevel Method for Minimizing the Lennard-Jones Potential |
title_full_unstemmed | Trilateration-Based Multilevel Method for Minimizing the Lennard-Jones Potential |
title_short | Trilateration-Based Multilevel Method for Minimizing the Lennard-Jones Potential |
title_sort | trilateration-based multilevel method for minimizing the lennard-jones potential |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7302545/ http://dx.doi.org/10.1007/978-3-030-50426-7_13 |
work_keys_str_mv | AT georgejithin trilaterationbasedmultilevelmethodforminimizingthelennardjonespotential AT dizichaowendy trilaterationbasedmultilevelmethodforminimizingthelennardjonespotential |