arXiv Machine Learning June 30, 2026 By Christian Munoz, Alexandre Tartakovsky

A Trainable-by-Parts Operator Learning Framework: Bridging DeepONet and Karhunen-Loeve Expansions for Large-Scale Applications

Read the original on arXiv Machine Learning →

arXiv:2606. 28519v1 Announce Type: new Abstract: Training operator-learning models for large-scale problems governed by partial differential equations (PDEs) is challenging due to the curse of dimensionality, memory constraints, and limited training data.

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

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