Neural Counterfactual Reasoning for Interacting Systems: Bridging Physics-Informed Learning and Reasoning for PHM

Over the past decade, advances in sensing and information technologies have enabled industries to collect large amounts of data. Yet, decision-making often remains driven by the intuition of domain experts who rely on simplistic analyses and short-term considerations. This frequently leads to suboptimal decisions that fail to account for long-term effects, particularly in complex, interconnected systems. Current data-driven strategies typically focus on immediate objectives, overlooking relational structures and longer-term impacts. There is a growing need for more transparent, generalizable models that can simulate system behavior, reason about alternative future scenarios, and extrapolate to unseen conditions—capabilities that are essential for decision-making in Prognostics andHealth Management (PHM). This research aims to advance reasoning and decision support in PHM through three novel contributions: (1) a physics-informed surrogate model for simulating rigid body interactions, enabling the exploration of ”what-if” scenarios, (2) an object-centric visual reasoning model for dynamics prediction in sensor-limited environments, supporting visual inspection tasks, and (3) a neuro-symbolic framework for interpretable root-cause analysis in time series, improving diagnostic transparency and providing actionable insights.