An Information-Theoretic Framework for Optimal Design: Analysis of Protocols for Estimating Soft Tissue Parameters in Biaxial Experiments
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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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Axioms |
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| Online Access: | https://www.mdpi.com/2075-1680/10/2/79 |
| _version_ | 1797535501403553792 |
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| author | Ankush Aggarwal Damiano Lombardi Sanjay Pant |
| author_facet | Ankush Aggarwal Damiano Lombardi Sanjay Pant |
| author_sort | Ankush Aggarwal |
| collection | DOAJ |
| 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 <i>k</i>-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. |
| first_indexed | 2024-03-10T11:46:31Z |
| format | Article |
| id | doaj.art-b40e60dd1e7d45ed85e320e9e4b3e738 |
| institution | Directory Open Access Journal |
| issn | 2075-1680 |
| language | English |
| last_indexed | 2024-03-10T11:46:31Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Axioms |
| spelling | doaj.art-b40e60dd1e7d45ed85e320e9e4b3e7382023-11-21T18:05:56ZengMDPI AGAxioms2075-16802021-05-011027910.3390/axioms10020079An Information-Theoretic Framework for Optimal Design: Analysis of Protocols for Estimating Soft Tissue Parameters in Biaxial ExperimentsAnkush Aggarwal0Damiano Lombardi1Sanjay Pant2Glasgow Computational Engineering Centre, James Watt School of Engineering, University of Glasgow, Glasgow G12 8LT, UKCOMMEDIA, Inria Paris, 2 rue Simone Iff, 75012 Paris, FranceZienkiewicz Centre for Computational Engineering, College of Engineering, Swansea University, Swansea SA1 8EN, UKA 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 <i>k</i>-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.https://www.mdpi.com/2075-1680/10/2/79optimal designsoft tissue mechanicsmutual informationbiaxial experimentinverse problemsinformation theory |
| spellingShingle | Ankush Aggarwal Damiano Lombardi Sanjay Pant An Information-Theoretic Framework for Optimal Design: Analysis of Protocols for Estimating Soft Tissue Parameters in Biaxial Experiments Axioms optimal design soft tissue mechanics mutual information biaxial experiment inverse problems information theory |
| title | 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_short | An Information-Theoretic Framework for Optimal Design: Analysis of Protocols for Estimating Soft Tissue Parameters in Biaxial Experiments |
| title_sort | information theoretic framework for optimal design analysis of protocols for estimating soft tissue parameters in biaxial experiments |
| topic | optimal design soft tissue mechanics mutual information biaxial experiment inverse problems information theory |
| url | https://www.mdpi.com/2075-1680/10/2/79 |
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