Open datasets and machine learning for two-phase heat transfer: a review following a spatial-temporal taxonomy
Read the original on arXiv Machine Learning →The review discusses how two‑phase heat transfer—critical for boiling, condensation, and thermal management—poses challenges for data reuse due to its complex interfacial physics. It surveys open datasets, machine‑learning techniques, and reusable software, organizing them with a spatial‑plus‑temporal dimensionality taxonomy (S+TD) that links data types to AI tasks such as regression, sequence learning, and image/video analysis. The paper proposes a roadmap for physics‑aware open data, including metadata standards, maturity labels, benchmark splits, and community databanks, emphasizing that progress in two‑phase AI relies as much on robust data infrastructure as on model design.
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