Detecting Inverse Boundaries by Weighted High-Order Gradient Collocation Method

Judy P. Yang and Hon Fung Samuel Lam
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Judy P. Yang: Department of Civil Engineering, National Chiao Tung University, Hsinchu 30010, Taiwan
Hon Fung Samuel Lam: Department of Civil Engineering, National Chiao Tung University, Hsinchu 30010, Taiwan

Mathematics, 2020, vol. 8, issue 8, 1-19

Abstract: The weighted reproducing kernel collocation method exhibits high accuracy and efficiency in solving inverse problems as compared with traditional mesh-based methods. Nevertheless, it is known that computing higher order reproducing kernel (RK) shape functions is generally an expensive process. Computational cost may dramatically increase, especially when dealing with strong-from equations where high-order derivative operators are required as compared to weak-form approaches for obtaining results with promising levels of accuracy. Under the framework of gradient approximation, the derivatives of reproducing kernel shape functions can be constructed synchronically, thereby alleviating the complexity in computation. In view of this, the present work first introduces the weighted high-order gradient reproducing kernel collocation method in the inverse analysis. The convergence of the method is examined through the weights imposed on the boundary conditions. Then, several configurations of multiply connected domains are provided to numerically investigate the stability and efficiency of the method. To reach the desired accuracy in detecting the outer boundary for two special cases, special treatments including allocation of points and use of ghost points are adopted as the solution strategy. From four benchmark examples, the efficacy of the method in detecting the unknown boundary is demonstrated.

Keywords: reproducing kernel approximation; gradient approximation; high-order gradient approximation; strong form collocation; inverse problem; multiply connected domain (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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