Tsukiji et al 2024a - Revision history

https://www.scipedia.com/wd/index.php?action=history&feed=atom&title=Tsukiji_et_al_2024a Tsukiji et al 2024a - Revision history 2026-05-07T18:30:55Z Revision history for this page on the wiki MediaWiki 1.27.0-wmf.10 https://www.scipedia.com/wd/index.php?title=Tsukiji_et_al_2024a&diff=305768&oldid=prev JSanchez: JSanchez moved page Draft Sanchez Pinedo 403170633 to Tsukiji et al 2024a 2024-07-01T12:42:48Z

<p>JSanchez moved page <a href="/public/Draft_Sanchez_Pinedo_403170633" class="mw-redirect" title="Draft Sanchez Pinedo 403170633">Draft Sanchez Pinedo 403170633</a> to <a href="/public/Tsukiji_et_al_2024a" title="Tsukiji et al 2024a">Tsukiji et al 2024a</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 12:42, 1 July 2024</td> </tr><tr><td colspan='2' style='text-align: center;' lang='en'><div class="mw-diff-empty">(No difference)</div> </td></tr></table>

JSanchez https://www.scipedia.com/wd/index.php?title=Tsukiji_et_al_2024a&diff=305767&oldid=prev JSanchez at 12:42, 1 July 2024 2024-07-01T12:42:41Z

<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 12:42, 1 July 2024</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'> </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>In this study, we propose a sub-voxel learning method based on a Neural Operator and predict the thermal temperature field on a circuit board in unsteady heat conduction. CAE analysis reduces the cost of experiments using prototypes in the design phase. However, the computational cost is high because the number of trials increases due to changes in analysis conditions. Predictions made by machine learning are less accurate than those made by CAE analysis, but they can significantly reduce computational costs. In general, machine learning is difficult to extrapolate. Thus, the concept of a Neural Operator has attracted attention as one machine learning method incorporating physical laws. In this study, we propose a sub-voxel learning method based on the Neural Operator, inspired by the forward Euler method, and predict the thermal temperature field on a circuit board during unsteady heat conduction. The input data is the analysis data of the current cycle step, and the output data is the data of the next cycle step. Dummy temperatures were set according to the heat generation of each IC in the input data of the 0th cycle step, and pseudo-difference values were generated to extract features. The prediction accuracy of the next cycle step was compared with and without dummy temperatures</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>In this study, we propose a sub-voxel learning method based on a Neural Operator and predict the thermal temperature field on a circuit board in unsteady heat conduction. CAE analysis reduces the cost of experiments using prototypes in the design phase. However, the computational cost is high because the number of trials increases due to changes in analysis conditions. Predictions made by machine learning are less accurate than those made by CAE analysis, but they can significantly reduce computational costs. In general, machine learning is difficult to extrapolate. Thus, the concept of a Neural Operator has attracted attention as one machine learning method incorporating physical laws. In this study, we propose a sub-voxel learning method based on the Neural Operator, inspired by the forward Euler method, and predict the thermal temperature field on a circuit board during unsteady heat conduction. The input data is the analysis data of the current cycle step, and the output data is the data of the next cycle step. Dummy temperatures were set according to the heat generation of each IC in the input data of the 0th cycle step, and pseudo-difference values were generated to extract features. The prediction accuracy of the next cycle step was compared with and without dummy temperatures</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> <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;">== Full Paper ==</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;"><pdf>Media:Draft_Sanchez Pinedo_403170633124.pdf</pdf></ins></div></td></tr>  </table>

JSanchez https://www.scipedia.com/wd/index.php?title=Tsukiji_et_al_2024a&diff=305765&oldid=prev JSanchez at 12:42, 1 July 2024 2024-07-01T12:42:39Z

<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 12:42, 1 July 2024</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"> </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;">In this study, we propose a sub-voxel learning method based on a Neural Operator and predict the thermal temperature field on a circuit board in unsteady heat conduction. CAE analysis reduces the cost of experiments using prototypes in the design phase. However, the computational cost is high because the number of trials increases due to changes in analysis conditions. Predictions made by machine learning are less accurate than those made by CAE analysis, but they can significantly reduce computational costs. In general, machine learning is difficult to extrapolate. Thus, the concept of a Neural Operator has attracted attention as one machine learning method incorporating physical laws. In this study, we propose a sub-voxel learning method based on the Neural Operator, inspired by the forward Euler method, and predict the thermal temperature field on a circuit board during unsteady heat conduction. The input data is the analysis data of the current cycle step, and the output data is the data of the next cycle step. Dummy temperatures were set according to the heat generation of each IC in the input data of the 0th cycle step, and pseudo-difference values were generated to extract features. The prediction accuracy of the next cycle step was compared with and without dummy temperatures</ins></div></td></tr>  </table>

JSanchez https://www.scipedia.com/wd/index.php?title=Tsukiji_et_al_2024a&diff=305764&oldid=prev JSanchez: Created blank page 2024-07-01T12:42:38Z

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