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An Information-Theoretic Framework for Optimal Design: Analysis of Protocols for Estimating Soft Tissue Parameters in Biaxial Experiments

Ankush Aggarwal Orcid Logo, Damiano Lombardi, Sanjay Pant Orcid Logo

Axioms, Volume: 10, Issue: 2, Start page: 79

Swansea University Authors: Ankush Aggarwal Orcid Logo, Sanjay Pant Orcid Logo

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DOI (Published version): 10.3390/axioms10020079

Abstract

A new framework for optimal design based on the information-theoretic measures of mutual information, conditional mutual information and their combination is proposed. The framework is tested on the analysis of protocols—a combination of angles along which strain measurements can be acquired—in a bi...

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Published in: Axioms
ISSN: 2075-1680
Published: MDPI AG 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa56721
first_indexed 2021-04-22T13:21:44Z
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spelling 2022-08-15T12:55:46.2894421 v2 56721 2021-04-22 An Information-Theoretic Framework for Optimal Design: Analysis of Protocols for Estimating Soft Tissue Parameters in Biaxial Experiments 33985d0c2586398180c197dc170d7d19 0000-0002-1755-8807 Ankush Aggarwal Ankush Aggarwal true false 43b388e955511a9d1b86b863c2018a9f 0000-0002-2081-308X Sanjay Pant Sanjay Pant true false 2021-04-22 A new framework for optimal design based on the information-theoretic measures of mutual information, conditional mutual information and their combination is proposed. The framework is tested on the analysis of protocols—a combination of angles along which strain measurements can be acquired—in a biaxial experiment of soft tissues for the estimation of hyperelastic constitutive model parameters. The proposed framework considers the information gain about the parameters from the experiment as the key criterion to be maximised, which can be directly used for optimal design. Information gain is computed through k-nearest neighbour algorithms applied to the joint samples of the parameters and measurements produced by the forward and observation models. For biaxial experiments, the results show that low angles have a relatively low information content compared to high angles. The results also show that a smaller number of angles with suitably chosen combinations can result in higher information gains when compared to a larger number of angles which are poorly combined. Finally, it is shown that the proposed framework is consistent with classical approaches, particularly D-optimal design. Journal Article Axioms 10 2 79 MDPI AG 2075-1680 optimal design; soft tissue mechanics; mutual information; biaxial experiment; inverse problems; information theory 1 5 2021 2021-05-01 10.3390/axioms10020079 COLLEGE NANME COLLEGE CODE Swansea University Engineering and Physical Sciences Research Council of the UK (Grant reference EP/R010811/1 to SP and grant reference EP/P018912/1 and EP/P018912/2 to AA). 2022-08-15T12:55:46.2894421 2021-04-22T14:16:38.9108159 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised Ankush Aggarwal 0000-0002-1755-8807 1 Damiano Lombardi 2 Sanjay Pant 0000-0002-2081-308X 3 56721__19794__81e60520717a4ea4b5c1670ee23574f6.pdf 56721(2).pdf 2021-05-04T13:16:17.7742255 Output 878085 application/pdf Version of Record true Copyright: © 2021 by the authors. This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY) true eng http://creativecommons.org/licenses/by/4.0/
title An Information-Theoretic Framework for Optimal Design: Analysis of Protocols for Estimating Soft Tissue Parameters in Biaxial Experiments
spellingShingle An Information-Theoretic Framework for Optimal Design: Analysis of Protocols for Estimating Soft Tissue Parameters in Biaxial Experiments
Ankush Aggarwal
Sanjay Pant
title_short An Information-Theoretic Framework for Optimal Design: Analysis of Protocols for Estimating Soft Tissue Parameters in Biaxial Experiments
title_full An Information-Theoretic Framework for Optimal Design: Analysis of Protocols for Estimating Soft Tissue Parameters in Biaxial Experiments
title_fullStr An Information-Theoretic Framework for Optimal Design: Analysis of Protocols for Estimating Soft Tissue Parameters in Biaxial Experiments
title_full_unstemmed An Information-Theoretic Framework for Optimal Design: Analysis of Protocols for Estimating Soft Tissue Parameters in Biaxial Experiments
title_sort An Information-Theoretic Framework for Optimal Design: Analysis of Protocols for Estimating Soft Tissue Parameters in Biaxial Experiments
author_id_str_mv 33985d0c2586398180c197dc170d7d19
43b388e955511a9d1b86b863c2018a9f
author_id_fullname_str_mv 33985d0c2586398180c197dc170d7d19_***_Ankush Aggarwal
43b388e955511a9d1b86b863c2018a9f_***_Sanjay Pant
author Ankush Aggarwal
Sanjay Pant
author2 Ankush Aggarwal
Damiano Lombardi
Sanjay Pant
format Journal article
container_title Axioms
container_volume 10
container_issue 2
container_start_page 79
publishDate 2021
institution Swansea University
issn 2075-1680
doi_str_mv 10.3390/axioms10020079
publisher MDPI AG
college_str Faculty of Science and Engineering
hierarchytype
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
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description A new framework for optimal design based on the information-theoretic measures of mutual information, conditional mutual information and their combination is proposed. The framework is tested on the analysis of protocols—a combination of angles along which strain measurements can be acquired—in a biaxial experiment of soft tissues for the estimation of hyperelastic constitutive model parameters. The proposed framework considers the information gain about the parameters from the experiment as the key criterion to be maximised, which can be directly used for optimal design. Information gain is computed through k-nearest neighbour algorithms applied to the joint samples of the parameters and measurements produced by the forward and observation models. For biaxial experiments, the results show that low angles have a relatively low information content compared to high angles. The results also show that a smaller number of angles with suitably chosen combinations can result in higher information gains when compared to a larger number of angles which are poorly combined. Finally, it is shown that the proposed framework is consistent with classical approaches, particularly D-optimal design.
published_date 2021-05-01T14:04:51Z
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