From State Reconstruction to State Interpretation: S2VMO, an Agentic Voxel-Based Digital Twin for Space Habitat Structural Monitoring

Abstract

Digital twins for space structures can estimate the physical states of systems with real-time data, yet long-term understanding and consistent analysis remain difficult. Current digital twin systems primarily aggregate data for monitoring and simulation, but they require experts to extract further insight, and most focus on state reconstruction and visualization, offering limited support for interpretation and decision-making. This study introduces the S2VMO (Sensor-to-Voxel Multi-Agentic Observatory), a framework that reconstructs structural state through finite element modeling (FEM) from four strain sensors by a two-stage neural surrogate that is stored as a graph of spatially indexed voxels. The shared voxel world model enables agents to identify anomalies, explain structural behavior, and provide inspection-oriented recommendations through natural-language interaction. This extends digital twins beyond passive monitoring toward explainable structural assessment and decision support for future space habitat operations.