Title:

Topological derivative and neural network for inverse problems of coupled models * Neural networks

Subtitle:

Raport Badawczy = Research Report ; RB/38/2016/02

Creator:

Lipnicka, Marta ; Szulc, Katarzyna ; Żochowski, Antoni

Publisher:

Instytut Badań Systemowych. Polska Akademia Nauk ; Systems Research Institute. Polish Academy of Sciences

Place of publishing:

Warszawa

Date issued/created:

2016

Description:

4, 14-18 pages ; 21 cm ; Bibliography p. 19-20

Abstract:

This paper considers a coupled model described by the domain bounded in R2 and decomposed into two sub-domains Ω and ω in such way that the interior sub-domain ω is surrounded by the exterior sub-domain Ω. In the interior sub-domain the physical phenomenon are described by the linear partial differential equation (PDE), and in the exterior domain the processes are governed by nonlinear PDEs subject to some external function. An example of such a system constitutes a gravity flow around an elastic obstacle. The goal of this paper is to propose the combination of neural network and information given by the topological derivative for solving such difficult problems or at least providing the initial approximation of the solutions. For a fixed number of holes, the differential equation was considered and solved.

Relation:

Raport Badawczy = Research Report

Resource type:

Text

Detailed Resource Type:

Report

Source:

RB-2016-38-02

Language:

eng

Language of abstract:

eng

Rights:

Creative Commons Attribution BY 4.0 license

Terms of use:

Copyright-protected material. [CC BY 4.0] May be used within the scope specified in Creative Commons Attribution BY 4.0 license, full text available at: ; -

Digitizing institution:

Systems Research Institute of the Polish Academy of Sciences

Original in:

Library of Systems Research Institute PAS

Projects co-financed by:

Operational Program Digital Poland, 2014-2020, Measure 2.3: Digital accessibility and usefulness of public sector information; funds from the European Regional Development Fund and national co-financing from the state budget.

Access:

Open