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arXiv AI By Henan Sun, Haitao Hu, Jin Liu, Jianfeng Zhang, Lujia Pan, Nuo Chen, Jia Li

PhyMo: A Physical-Field Modality for Multimodal AI4Physics

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The paper introduces PhyMo, a physics‑grounded multimodal framework that uses a physical‑field modality to represent heterogeneous measurements via PDE‑associated operators. It follows a three‑stage learning process: pretraining a physical‑field encoder with PDE residual supervision, aligning its representations with visual embeddings in a shared latent space, and applying downstream prediction heads to the fused multimodal representations. Experiments on five diverse physical datasets show that PhyMo outperforms the strongest baseline on each dataset, establishing its effectiveness for multimodal representation learning in AI for Physics.

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 AI.

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