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Probing the Stochastic Dynamics of Coronaviruses: Machine Learning Assisted Deep Computational Insights with Exploitable Dimensions

T. Mukhopadhyay, S. Naskar, K. K. Gupta, R. Kumar, S. Dey, Sondipon Adhikari

Advanced Theory and Simulations, Volume: 4, Issue: 7, Start page: 2000291

Swansea University Author: Sondipon Adhikari

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DOI (Published version): 10.1002/adts.202000291

Abstract

A machine learning assisted efficient, yet comprehensive characterization ofthe dynamics of coronaviruses, in conjunction with finite element (FE)approach, is presented. Without affecting the accuracy of prediction inlow-frequency vibration analysis, an equivalent model for the FE analysis ispropose...

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Published in: Advanced Theory and Simulations
ISSN: 2513-0390 2513-0390
Published: Wiley 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa57326
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spelling 2021-07-30T17:48:15.2023665 v2 57326 2021-07-15 Probing the Stochastic Dynamics of Coronaviruses: Machine Learning Assisted Deep Computational Insights with Exploitable Dimensions 4ea84d67c4e414f5ccbd7593a40f04d3 Sondipon Adhikari Sondipon Adhikari true false 2021-07-15 FGSEN A machine learning assisted efficient, yet comprehensive characterization ofthe dynamics of coronaviruses, in conjunction with finite element (FE)approach, is presented. Without affecting the accuracy of prediction inlow-frequency vibration analysis, an equivalent model for the FE analysis isproposed, based on which the natural frequencies corresponding to first threenon-rigid modes are analyzed. To quantify the inherent system-uncertaintyefficiently, Monte Carlo simulation is proposed in conjunction with themachine learning based FE computational framework for obtaining completeprobabilistic descriptions considering both individual and compound effect ofstochasticity. A variance based sensitivity analysis is carried out to enumeratethe relative significance of different material parameters corresponding tovarious constituting parts of the coronavirus structure. Using the modalcharacteristics like natural frequencies and mode shapes of the virus structureincluding their stochastic bounds, it is possible to readily identifycoronaviruses by comparing the experimentally measured dynamic responsesin terms of the peaks of frequency response function. Results from this first ofits kind study on coronaviruses along with the proposed generic machinelearning based approach will accelerate the detection of viruses and createefficient pathways toward future inventions leading to cure and containmentin the field of virology. Journal Article Advanced Theory and Simulations 4 7 2000291 Wiley 2513-0390 2513-0390 Coronaviruses, Machine Learning 1 7 2021 2021-07-01 10.1002/adts.202000291 COLLEGE NANME Science and Engineering - Faculty COLLEGE CODE FGSEN Swansea University SU Library paid the OA fee (TA Institutional Deal) UK-India Education and Research Initiative UKIERI/P1212 2021-07-30T17:48:15.2023665 2021-07-15T09:56:24.2059246 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised T. Mukhopadhyay 1 S. Naskar 2 K. K. Gupta 3 R. Kumar 4 S. Dey 5 Sondipon Adhikari 6 57326__20407__fd20bcc2f8de4bafafc9f2d5f493fd2f.pdf VOR.57326.pdf 2021-07-15T10:03:35.9990005 Output 1634510 application/pdf Version of Record true Copyright: The Authors. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. true eng http://creativecommons.org/licenses/by/4.0/
title Probing the Stochastic Dynamics of Coronaviruses: Machine Learning Assisted Deep Computational Insights with Exploitable Dimensions
spellingShingle Probing the Stochastic Dynamics of Coronaviruses: Machine Learning Assisted Deep Computational Insights with Exploitable Dimensions
Sondipon Adhikari
title_short Probing the Stochastic Dynamics of Coronaviruses: Machine Learning Assisted Deep Computational Insights with Exploitable Dimensions
title_full Probing the Stochastic Dynamics of Coronaviruses: Machine Learning Assisted Deep Computational Insights with Exploitable Dimensions
title_fullStr Probing the Stochastic Dynamics of Coronaviruses: Machine Learning Assisted Deep Computational Insights with Exploitable Dimensions
title_full_unstemmed Probing the Stochastic Dynamics of Coronaviruses: Machine Learning Assisted Deep Computational Insights with Exploitable Dimensions
title_sort Probing the Stochastic Dynamics of Coronaviruses: Machine Learning Assisted Deep Computational Insights with Exploitable Dimensions
author_id_str_mv 4ea84d67c4e414f5ccbd7593a40f04d3
author_id_fullname_str_mv 4ea84d67c4e414f5ccbd7593a40f04d3_***_Sondipon Adhikari
author Sondipon Adhikari
author2 T. Mukhopadhyay
S. Naskar
K. K. Gupta
R. Kumar
S. Dey
Sondipon Adhikari
format Journal article
container_title Advanced Theory and Simulations
container_volume 4
container_issue 7
container_start_page 2000291
publishDate 2021
institution Swansea University
issn 2513-0390
2513-0390
doi_str_mv 10.1002/adts.202000291
publisher Wiley
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Uncategorised{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Uncategorised
document_store_str 1
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description A machine learning assisted efficient, yet comprehensive characterization ofthe dynamics of coronaviruses, in conjunction with finite element (FE)approach, is presented. Without affecting the accuracy of prediction inlow-frequency vibration analysis, an equivalent model for the FE analysis isproposed, based on which the natural frequencies corresponding to first threenon-rigid modes are analyzed. To quantify the inherent system-uncertaintyefficiently, Monte Carlo simulation is proposed in conjunction with themachine learning based FE computational framework for obtaining completeprobabilistic descriptions considering both individual and compound effect ofstochasticity. A variance based sensitivity analysis is carried out to enumeratethe relative significance of different material parameters corresponding tovarious constituting parts of the coronavirus structure. Using the modalcharacteristics like natural frequencies and mode shapes of the virus structureincluding their stochastic bounds, it is possible to readily identifycoronaviruses by comparing the experimentally measured dynamic responsesin terms of the peaks of frequency response function. Results from this first ofits kind study on coronaviruses along with the proposed generic machinelearning based approach will accelerate the detection of viruses and createefficient pathways toward future inventions leading to cure and containmentin the field of virology.
published_date 2021-07-01T04:12:59Z
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