Contrastive, multimodal, and interpretable machine learning for photonics and beyond

Thomas Christensen; Charlotte Loh; Viggo Moro; Andrew Ma; Rumen Dangovski; Marin Soljačić

doi:10.1117/12.3024708

18 June 2024 Contrastive, multimodal, and interpretable machine learning for photonics and beyond

Thomas Christensen, Charlotte Loh, Viggo Moro, Andrew Ma, Rumen Dangovski, Marin Soljačić

Proceedings Volume PC13017, Machine Learning in Photonics; PC130170A (2024) https://doi.org/10.1117/12.3024708
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Abstract

I will present the trajectory of our work on the application of machine learning techniques to problems in photonic crystals and materials analysis. I will highlight our work on contrastive pre-training approaches for photonic crystal analysis, opportunities and techniques in multimodal pre-training for settings with multiple sources of complementary data, and, finally, interpretable machine learning systems with applications to topological materials analysis.

Conference Presentation

Citation Download Citation

Thomas Christensen, Charlotte Loh, Viggo Moro, Andrew Ma, Rumen Dangovski, and Marin Soljačić "Contrastive, multimodal, and interpretable machine learning for photonics and beyond", Proc. SPIE PC13017, Machine Learning in Photonics, PC130170A (18 June 2024); https://doi.org/10.1117/12.3024708

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