arXiv Machine Learning July 27, 2026 By Xiao Xue, Maida Wang, Mingyang Gao, Minh Chung, Peter V. Coveney

Explainable quantum-compressed machine learning for complex fluid flows

Read the original on arXiv Machine Learning →

arXiv:2607. 21688v1 Announce Type: cross Abstract: Machine-learning surrogates of physical systems face a paradox: explainable models facing the challenge of expressivity to capture complex nonlinear flows, whereas expressive deep surrogates match high-fidelity simulations only through massive parameterisations that turn the learned dynamics into a black box.

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

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