Adaptive Surrogate Modeling for Expedited Estimation of Nonlinear Tissue Properties Through Inverse Finite Element Analysis
Identifieur interne : 001132 ( Pmc/Curation ); précédent : 001131; suivant : 001133Adaptive Surrogate Modeling for Expedited Estimation of Nonlinear Tissue Properties Through Inverse Finite Element Analysis
Auteurs : Jason P. Halloran ; Ahmet ErdemirSource :
- Annals of biomedical engineering [ 0090-6964 ] ; 2011.
Abstract
Simulation-based prediction of specimen-specific biomechanical behavior commonly requires inverse analysis using geometrically consistent finite element (FE) models. Optimization drives such analyses but previous studies have highlighted a large computational cost dictated by iterative use of nonlinear FE models. The goal of this study was to evaluate the performance of a local regression-based adaptive surrogate modeling approach to decrease computational cost for both global and local optimization approaches using an inverse FE application. Nonlinear elastic material parameters for patient-specific heel-pad tissue were found, both with and without the surrogate model. Surrogate prediction replaced a FE simulation using local regression of previous simulations when the corresponding error estimate was less than a given tolerance. Performance depended on optimization type and tolerance value. The surrogate reduced local optimization expense up to 68%, but achieved accurate results for only 1 of 20 initial conditions. Conversely, up to a tolerance value of 20 N2, global optimization with the surrogate yielded consistent parameter predictions with a concurrent decrease in computational cost (up to 77%). However, the local optimization method without the surrogate, although sensitive to the initial conditions, was still on average seven times faster than the global approach. Our results help establish guide-lines for setting acceptable tolerance values while using an adaptive surrogate model for inverse FE analysis. Most important, the study demonstrates the benefits of a surrogate modeling approach for intensive FE-based iterative analysis.
Url:
DOI: 10.1007/s10439-011-0317-2
PubMed: 21544674
PubMed Central: 3150601
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<author><name sortKey="Erdemir, Ahmet" sort="Erdemir, Ahmet" uniqKey="Erdemir A" first="Ahmet" last="Erdemir">Ahmet Erdemir</name>
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<front><div type="abstract" xml:lang="en"><p id="P1">Simulation-based prediction of specimen-specific biomechanical behavior commonly requires inverse analysis using geometrically consistent finite element (FE) models. Optimization drives such analyses but previous studies have highlighted a large computational cost dictated by iterative use of nonlinear FE models. The goal of this study was to evaluate the performance of a local regression-based adaptive surrogate modeling approach to decrease computational cost for both global and local optimization approaches using an inverse FE application. Nonlinear elastic material parameters for patient-specific heel-pad tissue were found, both with and without the surrogate model. Surrogate prediction replaced a FE simulation using local regression of previous simulations when the corresponding error estimate was less than a given tolerance. Performance depended on optimization type and tolerance value. The surrogate reduced local optimization expense up to 68%, but achieved accurate results for only 1 of 20 initial conditions. Conversely, up to a tolerance value of 20 N<sup>2</sup>
, global optimization with the surrogate yielded consistent parameter predictions with a concurrent decrease in computational cost (up to 77%). However, the local optimization method without the surrogate, although sensitive to the initial conditions, was still on average seven times faster than the global approach. Our results help establish guide-lines for setting acceptable tolerance values while using an adaptive surrogate model for inverse FE analysis. Most important, the study demonstrates the benefits of a surrogate modeling approach for intensive FE-based iterative analysis.</p>
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<title-group><article-title>Adaptive Surrogate Modeling for Expedited Estimation of Nonlinear Tissue Properties Through Inverse Finite Element Analysis</article-title>
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<contrib-group><contrib contrib-type="author"><name><surname>Halloran</surname>
<given-names>Jason P.</given-names>
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<contrib contrib-type="author"><name><surname>Erdemir</surname>
<given-names>Ahmet</given-names>
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<aff id="A1">Computational Biomodeling (CoBi) Core and Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA</aff>
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<author-notes><corresp id="cor1">Address correspondence to Jason P. Halloran, Computational Biomodeling (CoBi) Core and Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA. <email>hallorj@ccf.org</email>
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<pub-date pub-type="nihms-submitted"><day>10</day>
<month>6</month>
<year>2011</year>
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<pub-date pub-type="epub"><day>5</day>
<month>5</month>
<year>2011</year>
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<pub-date pub-type="ppub"><month>9</month>
<year>2011</year>
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<pub-date pub-type="pmc-release"><day>1</day>
<month>9</month>
<year>2012</year>
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<volume>39</volume>
<issue>9</issue>
<fpage>2388</fpage>
<lpage>2397</lpage>
<permissions><copyright-statement>© 2011 Biomedical Engineering Society</copyright-statement>
<copyright-year>2011</copyright-year>
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<abstract><p id="P1">Simulation-based prediction of specimen-specific biomechanical behavior commonly requires inverse analysis using geometrically consistent finite element (FE) models. Optimization drives such analyses but previous studies have highlighted a large computational cost dictated by iterative use of nonlinear FE models. The goal of this study was to evaluate the performance of a local regression-based adaptive surrogate modeling approach to decrease computational cost for both global and local optimization approaches using an inverse FE application. Nonlinear elastic material parameters for patient-specific heel-pad tissue were found, both with and without the surrogate model. Surrogate prediction replaced a FE simulation using local regression of previous simulations when the corresponding error estimate was less than a given tolerance. Performance depended on optimization type and tolerance value. The surrogate reduced local optimization expense up to 68%, but achieved accurate results for only 1 of 20 initial conditions. Conversely, up to a tolerance value of 20 N<sup>2</sup>
, global optimization with the surrogate yielded consistent parameter predictions with a concurrent decrease in computational cost (up to 77%). However, the local optimization method without the surrogate, although sensitive to the initial conditions, was still on average seven times faster than the global approach. Our results help establish guide-lines for setting acceptable tolerance values while using an adaptive surrogate model for inverse FE analysis. Most important, the study demonstrates the benefits of a surrogate modeling approach for intensive FE-based iterative analysis.</p>
</abstract>
<kwd-group><kwd>Finite element modeling</kwd>
<kwd>Computer simulation</kwd>
<kwd>Tissue mechanics</kwd>
<kwd>Plantar tissue</kwd>
<kwd>Inverse modeling</kwd>
<kwd>Optimization</kwd>
</kwd-group>
<funding-group><award-group><funding-source country="United States">National Institute of Biomedical Imaging and Bioengineering : NIBIB</funding-source>
<award-id>R01 EB006735-01 || EB</award-id>
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