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		<title>Mishra et al 2026a - Revision history</title>
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		<updated>2026-05-25T11:53:20Z</updated>
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		<title>Scipediacontent: Scipediacontent moved page Draft content 153231436 to Mishra et al 2026a</title>
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				<updated>2026-05-21T08:30:21Z</updated>
		
		<summary type="html">&lt;p&gt;Scipediacontent moved page &lt;a href=&quot;/public/Draft_content_153231436&quot; class=&quot;mw-redirect&quot; title=&quot;Draft content 153231436&quot;&gt;Draft content 153231436&lt;/a&gt; to &lt;a href=&quot;/public/Mishra_et_al_2026a&quot; title=&quot;Mishra et al 2026a&quot;&gt;Mishra et al 2026a&lt;/a&gt;&lt;/p&gt;
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				&lt;td colspan='1' style=&quot;background-color: white; color:black; text-align: center;&quot;&gt;← Older revision&lt;/td&gt;
				&lt;td colspan='1' style=&quot;background-color: white; color:black; text-align: center;&quot;&gt;Revision as of 08:30, 21 May 2026&lt;/td&gt;
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		<author><name>Scipediacontent</name></author>	</entry>

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		<id>https://www.scipedia.com/wd/index.php?title=Mishra_et_al_2026a&amp;diff=331555&amp;oldid=prev</id>
		<title>Scipediacontent: Created page with &quot; == Abstract ==  &lt;p&gt;This paper presents an enhanced Least Squares Support Vector Machine (LS-SVM) approach for meshless and accurate solution of higher-order boundary value pr...&quot;</title>
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				<updated>2026-05-21T08:30:18Z</updated>
		
		<summary type="html">&lt;p&gt;Created page with &amp;quot; == Abstract ==  &amp;lt;p&amp;gt;This paper presents an enhanced Least Squares Support Vector Machine (LS-SVM) approach for meshless and accurate solution of higher-order boundary value pr...&amp;quot;&lt;/p&gt;
&lt;p&gt;&lt;b&gt;New page&lt;/b&gt;&lt;/p&gt;&lt;div&gt;&lt;br /&gt;
== Abstract ==&lt;br /&gt;
&lt;br /&gt;
&amp;lt;p&amp;gt;This paper presents an enhanced Least Squares Support Vector Machine (LS-SVM) approach for meshless and accurate solution of higher-order boundary value problems (BVPs) that commonly arise in structural mechanics, fluid dynamics, and other engineering fields. The discussed method formulates thirdand fourth-order linear and nonlinear ordinary differential equations (ODEs) as data-driven optimization problems, eliminating the need for traditional mesh-based discretization. Leveraging a Radial Basis Function (RBF) kernel and regularization-based control of model complexity, the LS-SVM captures complex solution behaviour while maintaining stability and smoothness. The meshless nature of the model ensures geometry-independence, making it suitable for irregular or multi-point boundary conditions. A comparative analysis with established machine learning techniques, including Ridge Regression (RR), classical SVM, Random Forest (RF), and Extreme Gradient Boosting (XGB), demonstrates the competitive accuracy, robustness, and efficiency of LSSVM. The results highlight its potential as a promising solver for nonlinear and multi-point problems where meshless methods are advantageous. The results highlight its potential as a promising solver for simulation-based workflows in computational mechanics and scientific computing, where adaptability, generalization, and reliability are critical.OPEN ACCESS Received: 25/08/2025 Accepted: 21/10/2025 Published: 16/04/2026&amp;lt;/p&amp;gt;&lt;br /&gt;
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== Document ==&lt;br /&gt;
&amp;lt;pdf&amp;gt;Media:Draft_content_153231436-2365-document.pdf&amp;lt;/pdf&amp;gt;&lt;/div&gt;</summary>
		<author><name>Scipediacontent</name></author>	</entry>

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