<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>6515</id>
    <completedYear/>
    <publishedYear>2017</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>203</pageFirst>
    <pageLast>204</pageLast>
    <pageNumber>2</pageNumber>
    <edition/>
    <issue>1</issue>
    <volume>17</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Adaptive Algorithms for Optimal Hip Implant Positioning</title>
    <abstract language="deu">In an aging society where the number of joint replacements rises, it is important to also increase the longevity of implants.&#13;
In particular hip implants have a lifetime of at most 15 years. This derives primarily from &#13;
pain due to implant migration, wear, inflammation, and dislocation, which is affected by &#13;
the positioning of the implant during the surgery. Current joint replacement practice uses &#13;
2D software tools and relies on the experience of surgeons. Especially the 2D tools fail to &#13;
take the patients’ natural range of motion as well as stress distribution in the 3D joint &#13;
induced by different daily motions into account.&#13;
Optimizing the hip joint implant position for all possible parametrized motions under the &#13;
constraint of a contact problem is prohibitively expensive as there are too many motions &#13;
and every position change demands a recalculation of the contact problem. For the &#13;
reduction of the computational effort, we use adaptive refinement on the parameter &#13;
domain coupled with the interpolation method of Kriging. A coarse initial grid is to be &#13;
locally refined using goal-oriented error estimation, reducing locally high  variances. This &#13;
approach will be combined with multi-grid optimization such that numerical errors are &#13;
reduced.</abstract>
    <parentTitle language="eng">PAMM</parentTitle>
    <identifier type="doi">10.1002/pamm.201710071</identifier>
    <enrichment key="PeerReviewed">no</enrichment>
    <author>Marian Moldenhauer</author>
    <submitter>Marian Moldenhauer</submitter>
    <author>Martin Weiser</author>
    <author>Stefan Zachow</author>
    <collection role="institutes" number="num">Numerical Mathematics</collection>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="compmed">Computational Medicine</collection>
    <collection role="persons" number="weiser">Weiser, Martin</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="ECMath-CH9">ECMath-CH9</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
</export-example>
