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PARC: Physics-aware recurrent convolutional neural networks to assimilate meso scale reactive mechanics of energetic materials
The thermo-mechanical response of shock-initiated energetic materials (EMs) is highly influenced by their microstructures, presenting an opportunity to engineer EM microstructures in a “materials-by-design” framework. However, the current design practice is limited, as a large ensemble of simulation...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Association for the Advancement of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10146890/ https://www.ncbi.nlm.nih.gov/pubmed/37115927 http://dx.doi.org/10.1126/sciadv.add6868 |
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author | Nguyen, Phong C. H. Nguyen, Yen-Thi Choi, Joseph B. Seshadri, Pradeep K. Udaykumar, H. S. Baek, Stephen S. |
author_facet | Nguyen, Phong C. H. Nguyen, Yen-Thi Choi, Joseph B. Seshadri, Pradeep K. Udaykumar, H. S. Baek, Stephen S. |
author_sort | Nguyen, Phong C. H. |
collection | PubMed |
description | The thermo-mechanical response of shock-initiated energetic materials (EMs) is highly influenced by their microstructures, presenting an opportunity to engineer EM microstructures in a “materials-by-design” framework. However, the current design practice is limited, as a large ensemble of simulations is required to construct the complex EM structure-property-performance linkages. We present the physics-aware recurrent convolutional (PARC) neural network, a deep learning algorithm capable of learning the mesoscale thermo-mechanics of EM from a modest number of high-resolution direct numerical simulations (DNS). Validation results demonstrated that PARC could predict the themo-mechanical response of shocked EMs with comparable accuracy to DNS but with notably less computation time. The physics-awareness of PARC enhances its modeling capabilities and generalizability, especially when challenged in unseen prediction scenarios. We also demonstrate that visualizing the artificial neurons at PARC can shed light on important aspects of EM thermos-mechanics and provide an additional lens for conceptualizing EM. |
format | Online Article Text |
id | pubmed-10146890 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Association for the Advancement of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-101468902023-04-29 PARC: Physics-aware recurrent convolutional neural networks to assimilate meso scale reactive mechanics of energetic materials Nguyen, Phong C. H. Nguyen, Yen-Thi Choi, Joseph B. Seshadri, Pradeep K. Udaykumar, H. S. Baek, Stephen S. Sci Adv Physical and Materials Sciences The thermo-mechanical response of shock-initiated energetic materials (EMs) is highly influenced by their microstructures, presenting an opportunity to engineer EM microstructures in a “materials-by-design” framework. However, the current design practice is limited, as a large ensemble of simulations is required to construct the complex EM structure-property-performance linkages. We present the physics-aware recurrent convolutional (PARC) neural network, a deep learning algorithm capable of learning the mesoscale thermo-mechanics of EM from a modest number of high-resolution direct numerical simulations (DNS). Validation results demonstrated that PARC could predict the themo-mechanical response of shocked EMs with comparable accuracy to DNS but with notably less computation time. The physics-awareness of PARC enhances its modeling capabilities and generalizability, especially when challenged in unseen prediction scenarios. We also demonstrate that visualizing the artificial neurons at PARC can shed light on important aspects of EM thermos-mechanics and provide an additional lens for conceptualizing EM. American Association for the Advancement of Science 2023-04-28 /pmc/articles/PMC10146890/ /pubmed/37115927 http://dx.doi.org/10.1126/sciadv.add6868 Text en Copyright © 2023 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY). https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution license (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Physical and Materials Sciences Nguyen, Phong C. H. Nguyen, Yen-Thi Choi, Joseph B. Seshadri, Pradeep K. Udaykumar, H. S. Baek, Stephen S. PARC: Physics-aware recurrent convolutional neural networks to assimilate meso scale reactive mechanics of energetic materials |
title | PARC: Physics-aware recurrent convolutional neural networks to assimilate meso scale reactive mechanics of energetic materials |
title_full | PARC: Physics-aware recurrent convolutional neural networks to assimilate meso scale reactive mechanics of energetic materials |
title_fullStr | PARC: Physics-aware recurrent convolutional neural networks to assimilate meso scale reactive mechanics of energetic materials |
title_full_unstemmed | PARC: Physics-aware recurrent convolutional neural networks to assimilate meso scale reactive mechanics of energetic materials |
title_short | PARC: Physics-aware recurrent convolutional neural networks to assimilate meso scale reactive mechanics of energetic materials |
title_sort | parc: physics-aware recurrent convolutional neural networks to assimilate meso scale reactive mechanics of energetic materials |
topic | Physical and Materials Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10146890/ https://www.ncbi.nlm.nih.gov/pubmed/37115927 http://dx.doi.org/10.1126/sciadv.add6868 |
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