Physics-Aware Digital Twin Reconstruction from Vision Streams Using Feature-Aligned Neural Scene Representations
Keywords:
digital twin, neural scene representation, physics-informed learning, feature alignment, vision streams, system architecture, governance, sustainabilityAbstract
The convergence of immersive sensing, neural scene representations, and physics-based simulation has opened a new frontier for digital twin systems that maintain synchrony with physical environments through continuous vision streams. This paper presents a system-level investigation of physics-aware digital twin reconstruction, emphasizing the integration of feature-aligned neural radiance fields and differentiable physics priors to produce operationally robust and semantically coherent virtual replicas. Departing from purely data-driven 3D reconstruction pipelines, the proposed paradigm embeds physical plausibility constraints into the learning process, enabling the resulting twin to support predictive simulation, anomaly detection, and closed-loop control. The manuscript examines architectural trade-offs between centralized and edge-federated reconstruction, the role of feature-space alignment for cross-view consistency, and the governance frameworks required to manage sensor drift, data heterogeneity, and model decay over extended deployments. By positioning the digital twin as a socio-technical infrastructure rather than a mere visualization tool, the discussion extends to fairness in sensor coverage, long-term sustainability of updating cycles, and the policy implications of automated decision-making on the basis of twin-inferred states. Case comparisons across manufacturing floors, urban street canyons, and offshore energy platforms illustrate how physical fidelity requirements shift with domain tolerance, latency budgets, and safety-criticality. The analysis reveals that feature-aligned neural representations, when combined with lightweight physics-informed regularizers, offer a promising middle ground between high-dimensional photorealistic reconstruction and real-time performance constraints. The paper concludes with a roadmap for future research on verification, accountability, and federated governance of highly autonomous digital twin ecosystems.
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