Semantic-Electromagnetic Inversion With Pretrained Multimodal Generative Model.

ABSTRACT: Across diverse domains of science and technology, electromagnetic (EM) inversion problems benefit from the ability to account for multimodal prior information to regularize their inherent ill-posedness. Indeed, besides priors that are formulated mathematically or learned from quantitative data, valuable prior information may be available in the form of text or images. Besides handling semantic multimodality, it is furthermore important to minimize the cost of adapting to a new physical measurement operator and to limit the requirements for costly labeled data. Here, these challenges are tackled with a frugal and multimodal semantic-EM inversion technique. The key ingredient is a multimodal generator of reconstruction results that can be pretrained, being agnostic to the physical measuremen

SUBMITTER: Chen Y 

PROVIDER: S-EPMC11558082 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature