AI for photonics and topological physics

Marin Soljacic

doi:10.1117/12.2678581

4 October 2023 AI for photonics and topological physics

Marin Soljacic

Proceedings Volume PC12647, Active Photonic Platforms (APP) 2023; PC1264714 (2023) https://doi.org/10.1117/12.2678581
Event: SPIE Nanoscience + Engineering, 2023, San Diego, California, United States

Abstract

I will present some novel AI techniques for photonics and physics in general. In particular, techniques which enable AI training with orders of magnitude less data (so-called few-shot learning techniques) will be discussed, including transfer learning and contrastive learning. Next, certain interpretable AI techniques will be discussed, including symbolic regression. Finally, our new concept of machine-learned chemical property (which we call “topogivity”) will be presented: roughly, topogivity of a given atom presents that material’s propensity to form topological materials.

Conference Presentation

Citation Download Citation

Marin Soljacic "AI for photonics and topological physics", Proc. SPIE PC12647, Active Photonic Platforms (APP) 2023, PC1264714 (4 October 2023); https://doi.org/10.1117/12.2678581

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