arXiv:2605.26502v1 Announce Type: new Abstract: The inverse problem of multilayer thin-film optical coatings design represents a complex combinatorial-continuous optimization challenge. We present PRISM (Position-encoded Regressive Inverse Spectral Model), a unified decoder-only autoregressive transformer that streamlines this process by jointly predicting discrete material selection and continuous thickness regression within a single backbone. PRISM introduces two primary architectural innovations: (1) spectrum prefix conditioning, which utilizes standard prefix tokens for in-context target i
The convergence of advanced AI models like transformers with complex scientific optimization problems is rapidly accelerating, leading to novel solutions in fields like materials science.
This development indicates a significant leap in AI's capability to automate and optimize the design of critical optical components, potentially reducing development cycles and costs dramatically.
The design process for multilayer thin-film optical coatings, traditionally a complex combinatorial problem, can now be streamlined and potentially revolutionized by autoregressive transformer models.
- · Optical materials industry
- · AI/ML research labs
- · Photonics sector
- · Defense and aerospace (for advanced optics)
- · Traditional manual design methodologies
- · Companies slow to adopt AI in R&D
PRISM accelerates the design and optimization of advanced optical coatings for various applications.
Faster and more efficient optical component design could lead to breakthroughs in areas such as sensing, communication, and energy technologies.
Democratization of advanced materials design tools could empower smaller research groups and startups, fostering innovation beyond well-resourced institutions.
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Read at arXiv cs.LG