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| Contribution to a conference proceedings/Contribution to a book | PUBDB-2025-04693 |
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2025
IEEE
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Please use a persistent id in citations: doi:10.1109/CLEO/Europe-EQEC65582.2025.11110889
Abstract: Ultrafast laser systems critically depend on optical thin film coatings to control dispersion and light propagation, enabling precise shaping of light. Optical thin film coating design represents a complex inverse problem traditionally relying on computationally intensive numerical optimization methods. These conventional approaches, exemplified by the widely used Needle Algorithm [1], require significant computational resources and often rely on expert intervention [2]. We here propose a new approach to these challenges and present a physics-informed machine learning framework based on an autoencoder architecture and use it to design an ultra-broadband dispersive mirror.
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