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computational modelling, machine learning and experimental validation to develop more efficient, durable and cost-effective green hydrogen technologies. Green hydrogen produced through water electrolysis
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systems combine models and observations yet still rely heavily on precomputed risk maps. Deep learning has improved flood mapping, but many models remain event-specific and transfer poorly between
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systems, their empirical security assessment remains underdeveloped, representing a growing and critical gap. Recent studies have highlighted vulnerabilities in learning‑enabled CPS, demonstrating
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