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cs.CE2026
Gradient-Informed Machine Learning in Electromagnetics
Matteo Zorzetto, Merle Backmeyer, Michael Wiesheu +3
Simulation techniques such as the finite element method are essential for designing electrical devices, but their computational cost can be prohibitive for repeated or real-time co…
cs.CE2026
Learning electromagnetic fields based on finite element basis functions
Merle Backmeyer, Michael Wiesheu, Sebastian Schöps
Parametric surrogate models of electric machines are widely used for efficient design optimization and operational monitoring. Addressing geometry variations, spline-based computer…
cs.CE2024
Solving Electromagnetic Scattering Problems by Isogeometric Analysis with Deep Operator Learning
Merle Backmeyer, Stefan Kurz, Matthias Möller +1
We present a hybrid approach combining isogeometric analysis with deep operator networks to solve electromagnetic scattering problems. The neural network takes a computer-aided des…