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arXiv Machine Learning By Niclas Flehmig, Mary Ann Lundteigen, Shen Yin

Uncertainty-aware AI-enabled framework for predicting hydrogen dispersion in confined spaces to enhance operator situational awareness

Read the original on arXiv Machine Learning →

The paper introduces a two‑stage AI framework that first estimates an unknown hydrogen leakage rate from early sensor data and then predicts the subsequent dispersion of the hydrogen cloud in confined maritime spaces. It outputs a concentration map with confidence bounds, enabling operators to assess the reliability of each forecast. The system, trained on an open dataset, achieves a mean absolute error of 0.052 g/s for leakage‑rate estimation and 0.018 for concentration prediction, approaching the sensor precision limit of ±0.01.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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