Physics-informed artificial intelligence for underground hydrogen storage: A PRISMA-based systematic review of validation, uncertainty, and decision readiness

Underground hydrogen storage is increasingly positioned as a long-duration flexibility option for renewable-dominated energy systems. Dependable deployment, however, is constrained by coupled uncertainties spanning multiphase flow, pore-scale interfacial physics, reactive transport, and potential microbiological effects. To improve the applicability and generalisability of physics-informed artificial intelligence for underground hydrogen storage, we conducted a PRISMA 2020 systematic literature review of studies published from 2020 to 2026. The Scopus and Google Scholar searches retrieved 1071 records. After deduplication, 872 unique records remained, and 676 full texts were assessed for eligibility. From these, 51 primary studies relevant to PI-AI for UHS were retained for synthesis after an evidence-quality appraisal gate, comprising 49 UHS-explicit studies and 2 methodologically adjacent studies, retained for transferable PI-AI insights. We examined the complexity of applying physics-informed learning across pore-to-field scales and how this complexity contributes to barriers to transferability, reproducibility, and decision readiness. Specifically, we synthesised evidence across five themes: pore-scale interfacial physics; reactive transport, sorption, and biogeochemistry; reservoir-scale surrogate architectures; uncertainty and data assimilation readiness; and multi-objective design and operational optimisation. Each study was also coded by task, model family, uncertainty practice, and validation strength. Only 9 of the 51 included studies (17.6%) reported predictive intervals or distributions consistent with quantified predictive uncertainty (UQ-A), and just one of these reported fully calibrated empirical-coverage checks, identifying uncertainty quantification as the most consequential evidence gap in the reviewed corpus.