https://www.scipedia.com/wd/index.php?action=history&feed=atom&title=Iwata_et_al_2024aIwata et al 2024a - Revision history2026-05-07T18:30:05ZRevision history for this page on the wikiMediaWiki 1.27.0-wmf.10https://www.scipedia.com/wd/index.php?title=Iwata_et_al_2024a&diff=305738&oldid=prevJSanchez: JSanchez moved page Draft Sanchez Pinedo 914338648 to Iwata et al 2024a2024-07-01T12:41:09Z<p>JSanchez moved page <a href="/public/Draft_Sanchez_Pinedo_914338648" class="mw-redirect" title="Draft Sanchez Pinedo 914338648">Draft Sanchez Pinedo 914338648</a> to <a href="/public/Iwata_et_al_2024a" title="Iwata et al 2024a">Iwata et al 2024a</a></p>
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</td></tr></table>JSanchezhttps://www.scipedia.com/wd/index.php?title=Iwata_et_al_2024a&diff=305737&oldid=prevJSanchez at 12:41, 1 July 20242024-07-01T12:41:03Z<p></p>
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<tr><td class='diff-marker'> </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'> </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'> </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>We are developing a high-speed simulation technology for physics simulations using deep learning. This technology aims to accelerate simulation time by a factor of several hundred to a thousand, significantly enhancing product performance and quality by increasing development efficiency and optimization. Currently, we are focusing on a multi-grid convolutional neural network (CNN) based architecture designed for models incorporating a combination of coarse and dense grids, addressing the challenge of model scaling and high resolution. Our previous work, reported by the Society for Computational Engineering and Science in June 2023, demonstrated the propagation of physical information from a coarse grid to a dense grid. Building on this foundation, we have now developed a technique that facilitates the propagation of physical information among multiple grids with varying resolutions. We applied this novel method to a basic temperature distribution prediction model for circuit boards and verified its high accuracy in predicting temperature distribution.</div></td><td class='diff-marker'> </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>We are developing a high-speed simulation technology for physics simulations using deep learning. This technology aims to accelerate simulation time by a factor of several hundred to a thousand, significantly enhancing product performance and quality by increasing development efficiency and optimization. Currently, we are focusing on a multi-grid convolutional neural network (CNN) based architecture designed for models incorporating a combination of coarse and dense grids, addressing the challenge of model scaling and high resolution. Our previous work, reported by the Society for Computational Engineering and Science in June 2023, demonstrated the propagation of physical information from a coarse grid to a dense grid. Building on this foundation, we have now developed a technique that facilitates the propagation of physical information among multiple grids with varying resolutions. We applied this novel method to a basic temperature distribution prediction model for circuit boards and verified its high accuracy in predicting temperature distribution.</div></td></tr>
<tr><td colspan="2"> </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>
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</table>JSanchezhttps://www.scipedia.com/wd/index.php?title=Iwata_et_al_2024a&diff=305735&oldid=prevJSanchez at 12:41, 1 July 20242024-07-01T12:41:02Z<p></p>
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<td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 12:41, 1 July 2024</td>
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<tr><td colspan="2"> </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"> </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'> </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'> </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"> </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;">We are developing a high-speed simulation technology for physics simulations using deep learning. This technology aims to accelerate simulation time by a factor of several hundred to a thousand, significantly enhancing product performance and quality by increasing development efficiency and optimization. Currently, we are focusing on a multi-grid convolutional neural network (CNN) based architecture designed for models incorporating a combination of coarse and dense grids, addressing the challenge of model scaling and high resolution. Our previous work, reported by the Society for Computational Engineering and Science in June 2023, demonstrated the propagation of physical information from a coarse grid to a dense grid. Building on this foundation, we have now developed a technique that facilitates the propagation of physical information among multiple grids with varying resolutions. We applied this novel method to a basic temperature distribution prediction model for circuit boards and verified its high accuracy in predicting temperature distribution.</ins></div></td></tr>
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</table>JSanchezhttps://www.scipedia.com/wd/index.php?title=Iwata_et_al_2024a&diff=305734&oldid=prevJSanchez: Created blank page2024-07-01T12:41:00Z<p>Created blank page</p>
<p><b>New page</b></p><div></div>JSanchez