Semantic-Electromagnetic Inversion With Pretrained Multimodal Generative Model.

Area of Science:

• Electromagnetic (EM) inversion
• Computational electromagnetics
• Applied physics

Background:

• Electromagnetic inversion problems are often ill-posed, requiring regularization.
• Prior information, including mathematical, quantitative, text, and image data, is crucial for regularization.
• Adapting inversion techniques to new physical operators and minimizing labeled data requirements are significant challenges.

Purpose of the Study:

• To develop a frugal and multimodal semantic-EM inversion technique.
• To address challenges in handling semantic multimodality and minimizing adaptation costs.
• To enable flexible, semantically controlled iterative solutions for inverse problems.

Main Methods:

• A multimodal generator pretrained to be agnostic to the physical measurement operator.
• A multimodal foundation model encoding semantic priors.
• A lightweight physical adapter, retrained for new physical settings.
• Iterative step-by-step solution with semantic control.

Main Results:

• Demonstrated feasibility and benefits of the proposed technique across diverse EM inverse problems.
• Successfully applied to 2D inverse-scattering, 3D, and 4D compressive microwave meta-imaging.
• Showcased effective regularization using multimodal semantic priors.

Conclusions:

• The proposed frugal and multimodal semantic-EM inversion technique offers an efficient approach to regularize ill-posed EM problems.
• The method effectively integrates diverse prior information, reduces adaptation costs, and allows for semantically guided solutions.
• This approach holds significant potential for advancing EM inversion applications in various scientific and technological domains.