CIFOM: A Physics-Informed Foundation Model Research Program for Critical Infrastructure: A Founding Thesis and Validation Framework

Industrial artificial intelligence has largely evolved under a data-first paradigm in which predictive models learn from historical sensor observations to detect anomalies and anticipate failures. Although effective in data-rich settings, this paradigm becomes fundamentally limited in critical infrastructure, where failures are rare, safety constraints prevent controlled experimentation, and representative fault data are inherently scarce. We argue that this limitation arises not only from insufficient data but also from the absence of explicit physical reasoning within current industrial learning systems. This paper presents the founding thesis of CIFOM (Critical Infrastructure Foundation Model), a long-term research program that investigates whether a physics-informed foundation model can learn the behavior of engineering assets rather than merely the statistical regularities of their sensor signals. Instead of treating industrial assets as collections of time series, CIFOM models them through an Engineering Asset Representation (EAR) that integrates asset identity, operational state, system context, operational memory, and codified engineering knowledge within a unified representation space. To support this objective, we propose a hybrid learning architecture in which immutable physical constraints, engineering knowledge, and data-driven learning are organized within a hierarchical constraint framework. The architecture combines deterministic conservation laws with Physics-Informed Neural Networks (PINNs) and Graph Neural Networks (GNNs) to jointly capture physical consistency and system-level relationships. We further introduce CIFOM Physics DataKit, a physics-consistent synthetic pretraining environment designed to generate scalable engineering scenarios for foundation model pretraining, together with a sim-to-real calibration protocol in which synthetic learning serves as an initialization stage rather than a replacement for real-world validation. As the first proof-of-concept, the research program focuses on centrifugal pumps owing to their widespread industrial deployment, well-characterized physical behavior, and rich multi-sensor observations. We position CIFOM within the emerging landscape of scientific foundation models and physics-informed artificial intelligence, and formulate four pre-registered research hypotheses addressing physical consistency, data efficiency, sim-to-real transfer, and cross-asset generalization. Rather than proposing another predictive maintenance algorithm, CIFOM introduces a research agenda for investigating whether reusable industrial learning substrates grounded in physical principles can serve as the foundation for the next generation of intelligent critical infrastructure systems.