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• W2005195748 abstract "This paper proposes an approach based on Least Squares Support Vector Machines (LS-SVMs) for solving second order partial differential equations (PDEs) with variable coefficients. Contrary to most existing techniques, the proposed method provides a closed form approximate solution. The optimal representation of the solution is obtained in the primal–dual setting. The model is built by incorporating the initial/boundary conditions as constraints of an optimization problem. The developed method is well suited for problems involving singular, variable and constant coefficients as well as problems with irregular geometrical domains. Numerical results for linear and nonlinear PDEs demonstrate the efficiency of the proposed method over existing methods." @default.
• W2005195748 created "2016-06-24" @default.
• W2005195748 creator A5076867569 @default.
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• W2005195748 date "2015-07-01" @default.
• W2005195748 modified "2023-09-27" @default.
• W2005195748 title "Learning solutions to partial differential equations using LS-SVM" @default.
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• W2005195748 doi "https://doi.org/10.1016/j.neucom.2015.02.013" @default.
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