41 citations · 41 across the 3 of their papers we have counts for
3 papers
physics.optics2025
A general framework for knowledge integration in machine learning for electromagnetic scattering using quasinormal modes
Viktor A. Lilja, Albin J. Svärdsby, Timo Gahlmann +1
Neural networks have been demonstrated to be able to accelerate the modeling and inverse design of optical and electromagnetic devices by serving as fast surrogates for electromagn…
physics.optics2025
Evaluation of machine learning techniques for conditional generative adversarial networks in inverse design
Timo Gahlmann, Philippe Tassin
Recently, machine learning has been introduced in the inverse design of physical devices, i.e., the automatic generation of device geometries for a desired physical response. In pa…
physics.comp-ph2022★ 41 cited
Deep neural networks for the prediction of the optical properties and the free-form inverse design of metamaterials
Timo Gahlmann, Philippe Tassin
Many phenomena in physics, including light, water waves, and sound, are described by wave equations. Given their coefficients, wave equations can be solved to high accuracy, but th…