http://www.colloquiam.com/wd/index.php?action=history&feed=atom&title=2023k 2023k - Revision history 2026-05-11T03:42:34Z Revision history for this page on the wiki MediaWiki 1.27.0-wmf.10 http://www.colloquiam.com/wd/index.php?title=2023k&diff=286661&oldid=prev JSanchez: JSanchez moved page Draft Sanchez Pinedo 255856511 to 2023k 2023-11-02T10:09:48Z <p>JSanchez moved page <a href="/public/Draft_Sanchez_Pinedo_255856511" class="mw-redirect" title="Draft Sanchez Pinedo 255856511">Draft Sanchez Pinedo 255856511</a> to <a href="/public/2023k" title="2023k">2023k</a></p> <table class="diff diff-contentalign-left" data-mw="interface"> <tr style='vertical-align: top;' lang='en'> <td colspan='1' style="background-color: white; color:black; text-align: center;">← Older revision</td> <td colspan='1' style="background-color: white; color:black; text-align: center;">Revision as of 10:09, 2 November 2023</td> </tr><tr><td colspan='2' style='text-align: center;' lang='en'><div class="mw-diff-empty">(No difference)</div> </td></tr></table> JSanchez http://www.colloquiam.com/wd/index.php?title=2023k&diff=286660&oldid=prev JSanchez at 10:09, 2 November 2023 2023-11-02T10:09:41Z <p></p> <table class="diff diff-contentalign-left" data-mw="interface"> <col class='diff-marker' /> <col class='diff-content' /> <col class='diff-marker' /> <col class='diff-content' /> <tr style='vertical-align: top;' lang='en'> <td colspan='2' style="background-color: white; color:black; text-align: center;">← Older revision</td> <td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 10:09, 2 November 2023</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l3" >Line 3:</td> <td colspan="2" class="diff-lineno">Line 3:</td></tr> <tr><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr> <tr><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>In the context of nonlinear multi-scale problems, the inverse estimation of macroscopic distribution of some microscopic parameters based on macroscopic measurements poses significant challenges. These challenges arise from (1) the high computational cost to solve the complex forward problem, and (2) the need for derivatives of the complex multi-scale forward model, which combines macro-scale and micro-scale simulations, both of which are typically nonlinear. To address these challenges, we propose a novel approach that combines ensemble Kalman inversion for derivative-free inverse estimation and a physics-informed deep learningbased model order reduction (DL-MOR) to accelerate the micro-scale simulation. We evaluate the performance of our method using a non-linear hyper-elastic model. The results demonstrate the effectiveness of DL-MOR in significantly speeding up the micro-scale simulation and enabling relatively accurate estimation of the microscopic parameter using only macro-scale boundary measurements.</div></td><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>In the context of nonlinear multi-scale problems, the inverse estimation of macroscopic distribution of some microscopic parameters based on macroscopic measurements poses significant challenges. These challenges arise from (1) the high computational cost to solve the complex forward problem, and (2) the need for derivatives of the complex multi-scale forward model, which combines macro-scale and micro-scale simulations, both of which are typically nonlinear. To address these challenges, we propose a novel approach that combines ensemble Kalman inversion for derivative-free inverse estimation and a physics-informed deep learningbased model order reduction (DL-MOR) to accelerate the micro-scale simulation. We evaluate the performance of our method using a non-linear hyper-elastic model. The results demonstrate the effectiveness of DL-MOR in significantly speeding up the micro-scale simulation and enabling relatively accurate estimation of the microscopic parameter using only macro-scale boundary measurements.</div></td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">== Full Paper ==</ins></div></td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">&lt;pdf&gt;Media:Draft_Sanchez Pinedo_255856511pap_293.pdf&lt;/pdf&gt;</ins></div></td></tr> <!-- diff cache key mw_drafts_scipedia-sc_mwd_:diff:version:1.11a:oldid:286658:newid:286660 --> </table> JSanchez http://www.colloquiam.com/wd/index.php?title=2023k&diff=286658&oldid=prev JSanchez at 10:09, 2 November 2023 2023-11-02T10:09:39Z <p></p> <table class="diff diff-contentalign-left" data-mw="interface"> <col class='diff-marker' /> <col class='diff-content' /> <col class='diff-marker' /> <col class='diff-content' /> <tr style='vertical-align: top;' lang='en'> <td colspan='2' style="background-color: white; color:black; text-align: center;">← Older revision</td> <td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 10:09, 2 November 2023</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l1" >Line 1:</td> <td colspan="2" class="diff-lineno">Line 1:</td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">&#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; </ins></div></td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">==Abstract==</ins></div></td></tr> <tr><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">In the context of nonlinear multi-scale problems, the inverse estimation of macroscopic distribution of some microscopic parameters based on macroscopic measurements poses significant challenges. These challenges arise from (1) the high computational cost to solve the complex forward problem, and (2) the need for derivatives of the complex multi-scale forward model, which combines macro-scale and micro-scale simulations, both of which are typically nonlinear. To address these challenges, we propose a novel approach that combines ensemble Kalman inversion for derivative-free inverse estimation and a physics-informed deep learningbased model order reduction (DL-MOR) to accelerate the micro-scale simulation. We evaluate the performance of our method using a non-linear hyper-elastic model. The results demonstrate the effectiveness of DL-MOR in significantly speeding up the micro-scale simulation and enabling relatively accurate estimation of the microscopic parameter using only macro-scale boundary measurements.</ins></div></td></tr> <!-- diff cache key mw_drafts_scipedia-sc_mwd_:diff:version:1.11a:oldid:286657:newid:286658 --> </table> JSanchez http://www.colloquiam.com/wd/index.php?title=2023k&diff=286657&oldid=prev JSanchez: Created blank page 2023-11-02T10:09:38Z <p>Created blank page</p> <p><b>New page</b></p><div></div> JSanchez