Zhengchang Hua

Zhengchang Hua, Panagiotis Oikonomou, Karim Djemame et al.

The coordination of large-scale, decentralised systems, such as a fleet of Electric Vehicles (EVs) in a Vehicle-to-Grid (V2G) network, presents a significant challenge for modern control systems. While collaborative Digital Twins have been proposed as a solution to manage such systems without compromising the privacy of individual agents, deriving globally optimal control policies from the high-level information they share remains an open problem. This paper introduces Digital Twin Assisted Multi-Agent Deep Deterministic Policy Gradient (DT-MADDPG) algorithm, a novel hybrid architecture that integrates a multi-agent reinforcement learning framework with a collaborative DT network. Our core contribution is a simulation-assisted learning algorithm where the centralised critic is enhanced by a predictive global model that is collaboratively built from the privacy-preserving data shared by individual DTs. This approach removes the need for collecting sensitive raw data at a centralised entity, a requirement of traditional multi-agent learning algorithms. Experimental results in a simulated V2G environment demonstrate that DT-MADDPG can achieve coordination performance comparable to the standard MADDPG algorithm while offering significant advantages in terms of data privacy and architectural decentralisation. This work presents a practical and robust framework for deploying intelligent, learning-based coordination in complex, real-world cyber-physical systems.

Shuyi Chen, Zhengchang Hua, Nikos Tziritas et al.

Terrestrial AI training faces an unsustainable energy and water crisis, positioning Orbital Data Centers (ODCs) as a "zero operational carbon" alternative. However, the sub-$10μ\text{s}$ communication latency required for distributed Large Language Model (LLM) training forces ODCs into extreme physical density, triggering a critical "Proximity-Thermal Paradox." As these high-density systems scale into Monolithic Structures or Proximity Swarms, they suffer from intense thermal-fluid crosstalk (heat traps in shared cooling loops) and thermal-radiative crosstalk (mutual heating that blocks deep-space cooling radiators). If left unmitigated, this persistent heat stagnation not only triggers severe thermal throttling that degrades training throughput, but also induces severe thermal fatigue, drastically shortening hardware lifespans and generating premature space e-waste. To make orbital AI truly sustainable, this position paper challenges traditional uniform load-sharing. We propose the Thermal-Aware Heterogeneity Thesis, which treats spatial cooling variances as a primary resource management dimension. Building on this, we introduce Thermal-Load Balancing (TLB), a software framework that dynamically migrates LLM workloads to the coolest available units based on instantaneous fluid temperatures or absorbed radiation. Our analysis demonstrates that TLB resolves thermal bottlenecks to restore Model Flops Utilization (MFU), while simultaneously reducing physical thermal stress. Extending the operational lifespan of orbital hardware is crucial to amortize the massive embodied carbon of rocket launches, outlining a necessary pathway to scale orbital AI without accelerating e-waste.