Solving boundary value problems of mathematical physics using radial basis function networks
| Autor: | Maxim V. Zhukov, Vladimir Gorbachenko |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Trust region
Mathematical optimization Radial basis function network Artificial neural network Activation function 010103 numerical & computational mathematics Inverse problem 01 natural sciences 010101 applied mathematics Computational Mathematics Radial basis function Boundary value problem 0101 mathematics Mathematical physics Mathematics |
| Zdroj: | Computational Mathematics and Mathematical Physics. 57:145-155 |
| ISSN: | 1555-6662 0965-5425 |
| DOI: | 10.1134/s0965542517010079 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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