Solving Electromagnetic Scattering Problems by Isogeometric Analysis with Deep Operator Learning
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Date
2024-10Type
- Report
ETH Bibliography
yes
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Abstract
We present a hybrid approach combining isogeometric analysis with deep operator networks to solve electromagnetic scattering problems. The neural network takes a computer-aided design representation as input and predicts the electromagnetic field in a de Rham conforming B-spline basis such that for example the tangential continuity of the electric field is respected. The physical problem is included in the loss function during training. Our numerical results demonstrate that a trained network accurately predicts the electric field, showing convergence to the analytical solution with optimal rate. Additionally, training on a variety of geometries highlights the network’s generalization capabilities, achieving small error increases when applied to new geometries not included in the training set. Show more
Publication status
publishedExternal links
Journal / series
SAM Research ReportVolume
Publisher
Seminar for Applied Mathematics, ETH ZurichSubject
Integral equations (IEs); Computer-aided design (CAD); Non-uniform rational B-spline (NURBS); Isogeometric Analysis (IGA); Electromagnetic modeling; Physics-informed neural netowrks (PINNs); Deep operator networks (DeepONets)Organisational unit
02501 - Seminar für Angewandte Mathematik / Seminar for Applied Mathematics
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ETH Bibliography
yes
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