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S. Ren¹, X. Zhang¹, H. Li¹, G. Chu¹, D. Chen¹, H. Bai¹ and C. Hu^1,2

¹University of Science and Technology Beijing, Beijing, China
²Engineering Research Center of Intelligent Supercomputing, Ministry of Education, Beijing, China

doi:10.4203/ccc.2.7.1

S. Ren, X. Zhang, H. Li, G. Chu, D. Chen, H. Bai, C. Hu, "Interpretable Feature Extraction for the Numerical Particle System", in B.H.V. Topping, P. Iványi, (Editors), "Proceedings of the Eleventh International Conference on Engineering Computational Technology", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 2, Paper 7.1, 2022, doi:10.4203/ccc.2.7.1

Keywords: particle system, feature extraction, machine learning, numerical simulation, physical interpretability, reactor pressure vessel.

Abstract

Particle system analysis is important but costly in solving many physical problems since particles are the basic composition of almost everything, and machine learning is increasingly used in optimization for numerical simulations. The most important and difficult job is to make the particle system understandable by the machine learning model, which usually called feature extraction. In this paper, a novel method and an accurate physical interpretation for feature extraction of cascade defects data, an important particle system in reactor pressure vessel analysis, was proposed. Four strategies were designed to extract features from cascade defects data in which the DAPP shows the best performance. This study shows that feature extraction based on physical information has a positive significance for the analysis of particle systems and provide a theoretical support for improving the physical interpretability of machine learning models on particle systems.

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Front Page Browse CCC CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Conferences ISSN 2753-3239 CCC: 2 PROCEEDINGS OF THE ELEVENTH INTERNATIONAL CONFERENCE ON ENGINEERING COMPUTATIONAL TECHNOLOGY Edited by: B.H.V. Topping and P. Iványi Paper 7.1 Interpretable Feature Extraction for the Numerical Particle System S. Ren¹, X. Zhang¹, H. Li¹, G. Chu¹, D. Chen¹, H. Bai¹ and C. Hu^1,2 ¹University of Science and Technology Beijing, Beijing, China ²Engineering Research Center of Intelligent Supercomputing, Ministry of Education, Beijing, China doi:10.4203/ccc.2.7.1 Full Bibliographic Reference for this paper S. Ren, X. Zhang, H. Li, G. Chu, D. Chen, H. Bai, C. Hu, "Interpretable Feature Extraction for the Numerical Particle System", in B.H.V. Topping, P. Iványi, (Editors), "Proceedings of the Eleventh International Conference on Engineering Computational Technology", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 2, Paper 7.1, 2022, doi:10.4203/ccc.2.7.1 Keywords: particle system, feature extraction, machine learning, numerical simulation, physical interpretability, reactor pressure vessel. Abstract Particle system analysis is important but costly in solving many physical problems since particles are the basic composition of almost everything, and machine learning is increasingly used in optimization for numerical simulations. The most important and difficult job is to make the particle system understandable by the machine learning model, which usually called feature extraction. In this paper, a novel method and an accurate physical interpretation for feature extraction of cascade defects data, an important particle system in reactor pressure vessel analysis, was proposed. Four strategies were designed to extract features from cascade defects data in which the DAPP shows the best performance. This study shows that feature extraction based on physical information has a positive significance for the analysis of particle systems and provide a theoretical support for improving the physical interpretability of machine learning models on particle systems. download the full-text of this paper (PDF, 6 pages, 235 Kb) go to the previous paper go to the next paper return to the table of contents return to the volume description
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