1 citations · 1 across the 3 of their papers we have counts for
4 papers
Symmetry-Informed Deep Learning for Electromagnetic Scattering
Viktor A. Lilja, Philippe Tassin
Deep learning can accelerate the modeling of electromagnetic devices by replacing costly simulations with neural networks trained to map design parameters to scattering parameters.…
Inverse-designed release-free optomechanical crystal with high photon-phonon coupling
David Hambraeus, Paul Burger, Johan Kolvik +2
Interactions between light and mechanics provide a powerful interface between optical and microwave-frequency signals, with applications spanning classical signal processing and qu…
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…
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…