Hydrogen is central to a low-carbon energy future, yet its high diffusivity and low density increase the risk of leakage, accumulation in confined spaces, and explosion. Accurate source term estimation (STE) based on sensor data is therefore critical for safe hydrogen operations. However, conventional STE methods require more sensors than potential leak sources—an assumption impractical for hydrogen systems, where extensive infrastructure and hydrogen's small molecular size create far more possible leak points than deployable sensors. Here, we present a sensor-efficient, data-driven STE framework that enables reliable leak localization and quantification under sparse sensing conditions. By integrating physics-informed modeling with data-driven inference, the method reconstructs leak source terms from limited hydrogen concentration measurements. The framework is validated using both high-fidelity computational fluid dynamics simulations of a hydrogen-fueled turbine generator and controlled experiments in a fuel cell vehicle garage. Remarkably, the STE is theoretically achievable with as few as two sensors, significantly reducing sensing requirements compared to existing approaches. This work provides a practical and scalable solution for real-time hydrogen leak source reconstruction in indoor industrial and community environments, advancing safety management and supporting the broader deployment of hydrogen energy systems.


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