arXiv Machine Learning July 7, 2026 By Amirpasha Hedayat, Laura Balzano, Karthik Duraisamy

In-span learning: adapting reduced-order models using their own predictions

Read the original on arXiv Machine Learning →

arXiv:2607. 02937v1 Announce Type: new Abstract: Reduced-order models compress high-dimensional dynamics into low-dimensional representations that can be evaluated rapidly, but they lose accuracy when online dynamics drift beyond the training data.

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

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