Scipediacontent at 08:57, 16 April 2026

2026-04-16T08:57:41Z

Scipediacontent: Scipediacontent moved page Review 434454955160 to Mishra Chakraverty 2026a

2026-03-23T09:51:50Z

Scipediacontent moved page Review 434454955160 to Mishra Chakraverty 2026a

Scipediacontent: Scipediacontent moved page Draft content 353414629 to Review 434454955160

2026-03-23T09:24:04Z

Scipediacontent moved page Draft content 353414629 to Review 434454955160

Scipediacontent: Created page with " == Abstract ==
This paper presents an enhanced Least Squares Support Vector Machine (LS-SVM) approach for meshless and accurate solution of higher-order boundary value pr..."

2026-03-23T09:23:52Z

Created page with " == Abstract == <p>This paper presents an enhanced Least Squares Support Vector Machine (LS-SVM) approach for meshless and accurate solution of higher-order boundary value pr..."

New page

== Abstract ==

<p>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</p>

== Document ==
<pdf>Media:Draft_content_353414629-8981-document.pdf</pdf>

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← Older revision	Revision as of 09:51, 23 March 2026
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← Older revision	Revision as of 09:24, 23 March 2026
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Mishra Chakraverty 2026a - Revision history

Scipediacontent at 08:57, 16 April 2026

Scipediacontent: Scipediacontent moved page Review 434454955160 to Mishra Chakraverty 2026a

Scipediacontent: Scipediacontent moved page Draft content 353414629 to Review 434454955160

Scipediacontent: Created page with " == Abstract == This paper presents an enhanced Least Squares Support Vector Machine (LS-SVM) approach for meshless and accurate solution of higher-order boundary value pr..."

Scipediacontent: Created page with " == Abstract ==
This paper presents an enhanced Least Squares Support Vector Machine (LS-SVM) approach for meshless and accurate solution of higher-order boundary value pr..."