A meshfree stabilized collocation method (SCM) based on reproducing kernel approximation

2020 ◽  
Vol 371 ◽  
pp. 113303 ◽  
Author(s):  
Lihua Wang ◽  
Zhihao Qian
Keyword(s):  
Collocation Method ◽  
Reproducing Kernel ◽  
Kernel Approximation

A subdomain collocation method based on Voronoi domain partition and reproducing kernel approximation

2007 ◽  
Vol 196 (13-16) ◽  
pp. 1958-1967 ◽  
Author(s):  
J.X. Zhou ◽  
M.E. Li ◽  
Z.Q. Zhang ◽  
W. Zou ◽  
L. Zhang
Keyword(s):  
Collocation Method ◽  
Reproducing Kernel ◽  
Kernel Approximation ◽  
Domain Partition ◽  
Voronoi Domain

Solving Inverse Laplace Equation with Singularity by Weighted Reproducing Kernel Collocation Method

2017 ◽  
Vol 09 (05) ◽  
pp. 1750065 ◽  
Author(s):  
Judy P. Yang ◽  
Pai-Chen Guan ◽  
Chia-Ming Fan
Keyword(s):  
Inverse Problems ◽  
Collocation Method ◽  
Reproducing Kernel ◽  
Cauchy Problems ◽  
Overdetermined System ◽  
Numerical Examples ◽  
Kernel Approximation ◽  
Laplace Equations ◽  
Laplace Type ◽  
Least Squares Minimization

This work introduces the weighted collocation method with reproducing kernel approximation to solve the inverse Laplace equations. As the inverse problems in consideration are equipped with over-specified boundary conditions, the resulting equations yield an overdetermined system. Following our previous work, the weighted collocation method using a least-squares minimization has shown to solve the inverse Cauchy problems efficiently without using techniques such as iteration and regularization. In this work, we further consider solving the inverse problems of Laplace type and introduce the Shepard functions to deal with singularity. Numerical examples are provided to demonstrate the validity of the method.

scholarly journals Detecting Inverse Boundaries by Weighted High-Order Gradient Collocation Method

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1297 ◽  
Author(s):  
Judy P. Yang ◽  
Hon Fung Samuel Lam
Keyword(s):  
Collocation Method ◽  
Reproducing Kernel ◽  
Outer Boundary ◽  
Computational Cost ◽  
Weak Form ◽  
High Order ◽  
Unknown Boundary ◽  
Shape Functions ◽  
High Order Derivative ◽  
Special Cases

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.

A gradient reproducing kernel collocation method for high order differential equations

2019 ◽  
Vol 64 (5) ◽  
pp. 1421-1454 ◽  
Author(s):  
Ashkan Mahdavi ◽  
Sheng-Wei Chi ◽  
Huiqing Zhu
Keyword(s):  
Differential Equations ◽  
Collocation Method ◽  
Reproducing Kernel ◽  
High Order

scholarly journals A reproducing kernel Hilbert space approach in meshless collocation method

2019 ◽  
Vol 38 (2) ◽  
Author(s):  
Babak Azarnavid ◽  
Mahdi Emamjome ◽  
Mohammad Nabati ◽  
Saeid Abbasbandy
Keyword(s):  
Hilbert Space ◽  
Collocation Method ◽  
Reproducing Kernel ◽  
Reproducing Kernel Hilbert Space ◽  
Meshless Collocation ◽  
Space Approach
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