https://www.scipedia.com/wd/index.php?action=history&feed=atom&title=Muraoka_Wada_2024aMuraoka Wada 2024a - Revision history2026-05-08T03:22:28ZRevision history for this page on the wikiMediaWiki 1.27.0-wmf.10https://www.scipedia.com/wd/index.php?title=Muraoka_Wada_2024a&diff=305756&oldid=prevJSanchez: JSanchez moved page Draft Sanchez Pinedo 553584547 to Muraoka Wada 2024a2024-07-01T12:42:08Z<p>JSanchez moved page <a href="/public/Draft_Sanchez_Pinedo_553584547" class="mw-redirect" title="Draft Sanchez Pinedo 553584547">Draft Sanchez Pinedo 553584547</a> to <a href="/public/Muraoka_Wada_2024a" title="Muraoka Wada 2024a">Muraoka Wada 2024a</a></p>
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<td colspan='1' style="background-color: white; color:black; text-align: center;">Revision as of 12:42, 1 July 2024</td>
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</td></tr></table>JSanchezhttps://www.scipedia.com/wd/index.php?title=Muraoka_Wada_2024a&diff=305755&oldid=prevJSanchez at 12:42, 1 July 20242024-07-01T12:42:03Z<p></p>
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<td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 12:42, 1 July 2024</td>
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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>This study presents a prediction of plural crack propagation using the discovered partial differential equations. 80% of structures fracture due to fatigue failure. Therefore, the evaluation of fatigue cracks is essential. Numerical analysis is costly, and machine-learning surrogate models have been proposed. Hence, the crack propagation path and remaining life are predicted using machine learning. A dataset is obtained from the results of a crack propagation analysis using a s-version FEM combined with an automatic mesh generation technique. The input parameters are the coordinates of the four crack tips, and the output parameters are the crack propagation vector and the number of cycles of 0.25 mm. Also, physics-informed neural networks (PINNs) have been widely studied in recent years. Thus, we took inspiration from PINNs and added a regularization term of PDE discovered by AI Feynman to the loss. As a result, the loss of a validation dataset for training constrained by PDE was reduced by about 77% compared to the unconstrained loss. The error in crack length decreased from -0.50% to 0.17%.</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>This study presents a prediction of plural crack propagation using the discovered partial differential equations. 80% of structures fracture due to fatigue failure. Therefore, the evaluation of fatigue cracks is essential. Numerical analysis is costly, and machine-learning surrogate models have been proposed. Hence, the crack propagation path and remaining life are predicted using machine learning. A dataset is obtained from the results of a crack propagation analysis using a s-version FEM combined with an automatic mesh generation technique. The input parameters are the coordinates of the four crack tips, and the output parameters are the crack propagation vector and the number of cycles of 0.25 mm. Also, physics-informed neural networks (PINNs) have been widely studied in recent years. Thus, we took inspiration from PINNs and added a regularization term of PDE discovered by AI Feynman to the loss. As a result, the loss of a validation dataset for training constrained by PDE was reduced by about 77% compared to the unconstrained loss. The error in crack length decreased from -0.50% to 0.17%.</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>
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</table>JSanchezhttps://www.scipedia.com/wd/index.php?title=Muraoka_Wada_2024a&diff=305753&oldid=prevJSanchez at 12:42, 1 July 20242024-07-01T12:42:01Z<p></p>
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<td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 12:42, 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;">This study presents a prediction of plural crack propagation using the discovered partial differential equations. 80% of structures fracture due to fatigue failure. Therefore, the evaluation of fatigue cracks is essential. Numerical analysis is costly, and machine-learning surrogate models have been proposed. Hence, the crack propagation path and remaining life are predicted using machine learning. A dataset is obtained from the results of a crack propagation analysis using a s-version FEM combined with an automatic mesh generation technique. The input parameters are the coordinates of the four crack tips, and the output parameters are the crack propagation vector and the number of cycles of 0.25 mm. Also, physics-informed neural networks (PINNs) have been widely studied in recent years. Thus, we took inspiration from PINNs and added a regularization term of PDE discovered by AI Feynman to the loss. As a result, the loss of a validation dataset for training constrained by PDE was reduced by about 77% compared to the unconstrained loss. The error in crack length decreased from -0.50% to 0.17%.</ins></div></td></tr>
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