New Trends in Electromagnetic Inverse Problems, Inverse Problems Special Issue

Oliver Dorn (1) , Yu Zhong (2, 3) , Dominique Lesselier (4)

1 University of Manchester [Manchester]
2 UiT - The Arctic University of Norway [Tromsø, Norway]
3 FINIAC Pte Ltd
4 L2S - Laboratoire des signaux et systèmes

Electromagnetic (EM) Inverse Problems have a long history of important medical, industrial and geophysical applications. The underlying theoretical and computational challenges have stimulated scientific research for more than a century and significant advances have been witnessed in this field regarding experimental capabilities, broadening fields of applications, multi-physics integration, novel modeling approaches, and increased computational capabilities for a reasonable cost, among others. This special issue is dedicated to highlighting such novel developments and trends, during the last 10 years or so, in this growing and exciting field of EM inverse problems.

Mots clés: Multi-physics tomography; Uncertainty Quantification; Neural Networks; Physics-Informed Neural Networks; Bayesian and Statistical Approaches; Regularization; Big-Data inversion; Reduced model; Optimal transport; Inverse design; Inverse radiative transfer

Domaines: Sciences de l'ingénieur [physics]; Informatique [cs]; Physique [physics]

Oliver Dorn, Yu Zhong, Dominique Lesselier. New Trends in Electromagnetic Inverse Problems, Inverse Problems Special Issue. Inverse Problems, 2023. ⟨hal-04108656⟩