Solving boundary value problems of mathematical physics using radial basis function networks
Žurnal vyčislitelʹnoj matematiki i matematičeskoj fiziki, Tome 57 (2017) no. 1, pp. 133-143 Cet article a éte moissonné depuis la source Math-Net.Ru

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A neural network method for solving boundary value problems of mathematical physics is developed. In particular, based on the trust region method, a method for learning radial basis function networks is proposed that significantly reduces the time needed for tuning their parameters. A method for solving coefficient inverse problems that does not require the construction and solution of adjoint problems is proposed.
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V. I. Gorbachenko; M. V. Zhukov. Solving boundary value problems of mathematical physics using radial basis function networks. Žurnal vyčislitelʹnoj matematiki i matematičeskoj fiziki, Tome 57 (2017) no. 1, pp. 133-143. http://geodesic.mathdoc.fr/item/ZVMMF_2017_57_1_a11/

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