用大模型与物理约束结合，自动设计节能数据中心布局
Phythesis: Physics-Guided Evolutionary Scene Synthesis for Energy-Efficient Data Center Design via LLMs
- 大模型生成布局并自我优化，物理引擎约束合理性
- 生成成功率提升57.3%，电力效率（PUE）改善11.5%
- 适合需高能效、强物理合规性的数据中心设计者
数据中心基础设施是支撑日益增长算力需求的基石。传统设计方法依赖人工经验与专用仿真工具，在系统复杂度上升时难以扩展。近期研究采用生成式人工智能设计符合人体工学的室内布局，但未考虑底层物理规律，难以满足数据中心对量化运行目标和严格物理约束的要求。为此，我们提出Phythesis框架，融合大语言模型（LLMs）与物理引导的进化优化，实现面向能源高效的模拟就绪（SimReady）场景自动生成。该框架采用迭代双层优化结构：(i) LLM驱动的优化层生成三维布局并自我批判以优化场景拓扑；(ii) 物理信息优化层确定最优设备参数并选择最佳设备组合。在三种生成规模上的实验表明，Phythesis相比基线LLM方案，生成成功率提升57.3%，功率使用效率（PUE）改善11.5%。
原文摘要 · Abstract (English)
Data center (DC) infrastructure serves as the backbone to support the escalating demand for computing capacity. Traditional design methodologies that blend human expertise with specialized simulation tools scale poorly with the increasing system complexity. Recent studies adopt generative artificial intelligence to design plausible human-centric indoor layouts. However, they do not consider the underlying physics, making them unsuitable for the DC design that sets quantifiable operational objectives and strict physical constraints. To bridge the gap, we propose Phythesis, a novel framework that synergizes large language models (LLMs) and physics-guided evolutionary optimization to automate simulation-ready (SimReady) scene synthesis for energy-efficient DC design. Phythesis employs an iterative bi-level optimization architecture, where (i) the LLM-driven optimization level generates physically plausible three-dimensional layouts and self-criticizes them to refine the scene topology, and (ii) the physics-informed optimization level identifies the optimal asset parameters and selects the best asset combination. Experiments on three generation scales show that Phythesis achieves 57.3% generation success rate increase and 11.5% power usage effectiveness (PUE) improvement, compared with the vanilla LLM-based solution.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。