Fusion Intelligence for Digital Twinning AI Data Centers: A Synergistic GenAI-PhyAI Approach
Ruihang Wang, Minghao Li, Zhiwei Cao, Jimin Jia, Kyle Guan, Yonggang Wen

TL;DR
This paper introduces Fusion Intelligence, a synergistic framework combining generative AI and physical AI to automate and optimize digital twin creation for AI data centers, enhancing accuracy and operational efficiency.
Contribution
It presents a novel dual-agent approach that integrates GenAI and PhyAI for automated digital twin generation and physical constraint enforcement in AI data centers.
Findings
Automated digital twin generation via GenAI improves efficiency.
PhyAI-enforced constraints enhance digital twin accuracy.
Framework supports power optimization and real-time data assimilation.
Abstract
The explosion in artificial intelligence (AI) applications is pushing the development of AI-dedicated data centers (AIDCs), creating management challenges that traditional methods and standalone AI solutions struggle to address. While digital twins are beneficial for AI-based design validation and operational optimization, current AI methods for their creation face limitations. Specifically, physical AI (PhyAI) aims to capture the underlying physical laws, which demands extensive, case-specific customization, and generative AI (GenAI) can produce inaccurate or hallucinated results. We propose Fusion Intelligence, a novel framework synergizing GenAI's automation with PhyAI's domain grounding. In this dual-agent collaboration, GenAI interprets natural language prompts to generate tokenized AIDC digital twins. Subsequently, PhyAI optimizes these generated twins by enforcing physical…
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Taxonomy
TopicsBig Data and Business Intelligence
