arXiv Machine Learning July 27, 2026 By Ingvild Askim Adde, Mary M. Maleckar, Gabriel Balaban

Latent PDE mapping for efficient physics-informed learning across geometries with limited data

Read the original on arXiv Machine Learning →

arXiv:2607. 22215v1 Announce Type: new Abstract: In this study, we introduce latent PDE mapping, a broadly applicable physics-informed learning technique designed to enable efficient geometric generalization with sparse training data.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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