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arXiv Machine Learning By Dex Doksoo Lee, Jehyeon Shin, Yue Yu, Sunkyu Yu, Junsuk Rho, Wei Chen

Learning Nonlocal Interactions in Large-Scale Heterogeneous Metamaterials through Scientific Machine Learning

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The paper introduces DANO, a domain‑decomposition attention neural operator that learns configuration‑dependent nonlocal interactions in heterogeneous systems governed by linear field equations using only field data. It demonstrates full‑field predictions for large‑scale optical metasurfaces, including transfer across domain sizes and inverse design, while validating the learned interactions through physics‑based interrogation. The work positions DANO as an AI‑for‑Science tool for discovering interactions and enabling scalable, customizable design of nonlocal multiscale systems such as virtual‑reality optics.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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