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Inverse mathematical physics problems solving by normalized radial basis function networks

Keywords:

I.S. Kolbin – Ph.D. (Phys.-Math.), Moscow Aviation Institute (Technical University) D.L. Reviznikov – D.Sci. (Phys.-Math.), Moscow Aviation Institute (Technical University)


In this paper we propose the method for solving inverse problems of mathematical physics. The method is based on normalized radial basis function networks. We discuss problems of source term identification and boundary conditions identification. The coefficients of neural network model are tuned by means of unconstrained multidimensional optimization. The efficiency of suggested approach is demonstrated on representative set of stationary and non-stationary inverse problems for heat transfer equations.
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