Thermodynamic Digital Twins: Physics-Native Semantic Synchronization for 6G Kinetic Swarms

2026-10-04

AI Summary

This study addresses the signaling overhead bottleneck in digital twin synchronization for large-scale dynamic clusters, caused by redundant reporting of predictable motion. We propose a thermodynamics-driven semantic synchronization framework that leverages divergence-entropy relationships to detect structural mutations and employs Reynolds decomposition to decouple macroscopic drift from microscopic corrections. Furthermore, smoothed particle hydrodynamics (SPH) descriptors are introduced to construct an event-triggered early-warning mechanism, which, combined with closed-loop delay compensation, enables physics-guided field-state extrapolation and on-demand correction. This framework significantly reduces edge-to-cloud synchronization payloads while achieving an optimal trade-off between tracking accuracy and communication cost, thereby effectively enhancing the latency tolerance of control loops.

Abstract

The synchronization of large-scale kinetic swarms with digital twin networks can generate substantial signaling overhead when predictable motion is repeatedly reported. To reduce such redundancy, we propose the thermodynamic digital twin (TDT), a physics-guided synchronization framework that combines field-based state extrapolation with event-triggered correction. For a closed domain with vanishing boundary flux, the divergence--entropy relation is used as a physical motivation for identifying structural changes in the reconstructed swarm field. Based on Reynolds decomposition, the hierarchical thermodynamic synchronization (HTS) protocol separates low-dimensional macroscopic drift updates from full-state microscopic corrections. A locally measurable smoothed-particle-hydrodynamics descriptor, combining velocity divergence and acceleration, provides an implementation-oriented early-warning trigger, while a transport-error analysis provides a short-interval rule for limiting silent extrapolation. The same extrapolation mechanism is further used as a predictor-like delay compensator in the closed-loop digital twin. Across the evaluated numerical settings, TDT reduces the generated edge-to-cloud synchronization payload relative to the considered baselines and exhibits a favorable empirical trade-off between tracking error and communication cost. The results also indicate improved delay tolerance in the evaluated control loop.

Problem

• Digital Twin
• Kinetic Swarms
• Signaling Overhead
• Synchronization
• 6G

Innovation

• Thermodynamic Digital Twin
• Reynolds Decomposition
• Event-triggered Synchronization
• Smoothed Particle Hydrodynamics
• Delay Compensation