E
E.J. Kansa
Researcher at University of California, Davis
Publications - 42
Citations - 8163
E.J. Kansa is an academic researcher from University of California, Davis. The author has contributed to research in topics: Partial differential equation & Elliptic partial differential equation. The author has an hindex of 25, co-authored 41 publications receiving 7669 citations. Previous affiliations of E.J. Kansa include Lawrence Livermore National Laboratory & Embry–Riddle Aeronautical University.
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Multiquadrics--a scattered data approximation scheme with applications to computational fluid-dynamics-- ii solutions to parabolic, hyperbolic and elliptic partial differential equations
TL;DR: In this paper, the authors used MQ as the spatial approximation scheme for parabolic, hyperbolic and the elliptic Poisson's equation, and showed that MQ is not only exceptionally accurate, but is more efficient than finite difference schemes which require many more operations to achieve the same degree of accuracy.
Approximation scheme with applications to computational fluid-dynamics-- i surface approximations and partial derivative estimates
TL;DR: In this article, the authors presented an enhanced multiquadrics (MQ) scheme for spatial approximations, which is a true scattered data, grid free scheme for representing surfaces and bodies in an arbitrary number of dimensions.
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Multiquadrics—A scattered data approximation scheme with applications to computational fluid-dynamics—I surface approximations and partial derivative estimates
TL;DR: In this article, the authors presented a powerful, enhanced multiquadrics (MQ) scheme developed for spatial approximations, which is a true scattered data, grid free scheme for representing surfaces and bodies in an arbitrary number of dimensions.
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Circumventing the ill-conditioning problem with multiquadric radial basis functions: Applications to elliptic partial differential equations
E.J. Kansa,Y.C. Hon +1 more
TL;DR: This paper explores several techniques, each of which improves the conditioning of the coefficient matrix and the solution accuracy, and recommends using what has been learned from the FEM practitioners and combining their methods with what has be learned in RBF simulations to form a flexible, hybrid approach to solve complex multidimensional problems.
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Exponential convergence and H‐c multiquadric collocation method for partial differential equations
TL;DR: The radial basis function (RBF) collocation method as discussed by the authors uses global shape functions to interpolate and collocatethe approximate solution of PDEs, which is a truly meshless method as compared to some of the so-calledmeshless or element-free element methods.