Investigation of Multiply Connected Inverse Cauchy Problems by Efficient Weighted Collocation Method

This work introduces an efficient weighted collocation method to solve inverse Cauchy problems. As it is known that the reproducing kernel approximation takes time to compute the second-order derivatives in the meshfree strong form method, the gradient approach alleviates such a drawback by approximating the first-order derivatives in a similar way to the primary unknown. In view of the overdetermined system derived from inverse Cauchy problems with incomplete boundary conditions, the weighted gradient reproducing kernel collocation method (G-RKCM) is further introduced in the analysis. The convergence of the method is first demonstrated by the simply connected inverse problems, in which the same set of source points and collocation points is adopted. Then, the multiply connected inverse problems are investigated to show that high accuracy of approximation can be reached. The sensitivity and stability of the method is tested through the disturbance added on both Neumann and Dirichlet boundary conditions. From the investigation of four benchmark problems, it is concluded that the weighted gradient reproducing kernel collocation method is more efficient than the reproducing kernel collocation method.