On the acceleration of the numerical solution of partial differential equations using radial basis functions and graphics processing units
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[1] Pat Hanrahan,et al. Brook for GPUs: stream computing on graphics hardware , 2004, SIGGRAPH 2004.
[2] E. Kansa,et al. Exponential convergence and H‐c multiquadric collocation method for partial differential equations , 2003 .
[3] Ioannis T. Rekanos. On-line inverse scattering of conducting cylinders using radial basis-function neural networks , 2001 .
[4] Liu Guosui,et al. Radar target classification based on radial basis function and modified radial basis function networks , 1996, Proceedings of International Radar Conference.
[5] David Tarditi,et al. Accelerator: using data parallelism to program GPUs for general-purpose uses , 2006, ASPLOS XII.
[6] Satish S. Udpa,et al. Solution of inverse problems in electromagnetics using Hopfield neural networks , 1995 .
[7] Salvatore Caorsi,et al. A threshold electromagnetic classification approach for cylinders embedded in a lossy medium by using a neural network technique , 2000 .
[9] Alexander I. Fedoseyev,et al. Continuation for nonlinear Elliptic Partial differential equations discretized by the multiquadric Method , 2000, Int. J. Bifurc. Chaos.
[10] Krzysztof A. Michalski,et al. A neural-network approach to the electromagnetic imaging of elliptic conducting cylinders , 2001 .