Efficient numerical solution of boundary identification problems: MFS with adaptive stochastic optimization
| Autor: | Michael Vynnycky, P. Nanda, José Alberto Cuminato, Gujji Murali Mohan Reddy |
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| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Uncertain data Computer science Applied Mathematics Stability (learning theory) Boundary (topology) Estimator 020206 networking & telecommunications 02 engineering and technology Parameter identification problem Tikhonov regularization Computational Mathematics 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering PROBLEMAS DE CONTORNO Method of fundamental solutions Applied mathematics Stochastic optimization |
| Zdroj: | Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual) Universidade de São Paulo (USP) instacron:USP |
| Popis: | In this article, we study a novel computational technique for the efficient numerical solution of the inverse boundary identification problem with uncertain data in two dimensions. The method essentially relies on a posteriori error indicators consisting of the Tikhonov regularized solutions obtained by the method of fundamental solutions (MFS) and the given data for the problem in hand. For a desired accuracy, the a posteriori error estimator chooses the best possible combination of a complete set of fundamental solutions determined by the location of the sources that are arranged in a particular manner on a pseudo-boundary at each iteration. Also, since we are interested in a stable solution, an adaptive stochastic optimization strategy based on an error-balancing criterion is used, so as to avoid unstable regions where the stability contributions may be relatively large. These ideas are applied to two benchmark problems and are found to produce efficient and accurate results. |
| Databáze: | OpenAIRE |
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