Physics-informed inverse design of ultrafast coatings: from direct optimization to generalizable fine-tuning
Publikationstyp
Conference Paper
Date Issued
2026
Sprache
English
Author(s)
Article Number
SW1A.6
Citation
Conference on Lasers and Electro-Optics, CLEO 2026
Contribution to Conference
Publisher DOI
Scopus ID
Publisher
Optica Publishing Group
ISBN of container
978-1-957171-58-6
We present a physics-informed neural network for optical coating design. We demonstrate state-of-the-art chirped mirror optimization and introduce a generalist model with parameter-efficient fine-tuning to rapidly generate high-precision designs for arbitrary targets.
DDC Class
005: Computer Programming, Programs, Data and Security
600: Technology