AI 中文总结
针对现代设计优化忽视计算开销的问题，本文在贝叶斯优化中引入能量足迹指标，引导参数配置兼顾性能与节俭，实验表明可同时获得更优解和更低能耗。
AI 中文摘要
现代设计优化框架的首要目标是获得预测最准确的模型，而不平衡计算开销。这也是即使采用样本高效的贝叶斯优化器，规模化架构和多学科设计优化问题仍然难以解决的原因之一。本文在贝叶斯优化框架中引入了一个量化计算能量足迹的指标，以引导模型的参数设置朝向兼顾性能与节俭性的配置。计算机实验揭示了最优收敛与潜在能量足迹之间存在的权衡，有时甚至同时实现了更优的最优点和更低的能耗。
英文摘要
Modern design optimization frameworks aim first and foremost for models with the most accurate predictions without balancing computational overhead. It remains a reason why scaled architecture and multidisciplinary design optimization problems are difficult to address, even with sample-efficient Bayesian optimizers. In this paper, a metric quantifying the computational energy footprint is introduced within a Bayesian optimization framework to guide the parameter setting of a model towards configurations that balance both performance and frugality. The computer experiments highlighted existing tradeoffs between optimum convergence and the underlying energy footprint, and sometimes resulted in both a better-found optimum and lower energy consumption.
CommentsPublished in MATEC Web of Conferences 422, 02009. 7th International Conference on Engineering Optimization
Journal refG. Plat, P. Saves, N. Bartoli, T. Lefebvre, J. Morlier, Energy-aware frugal Bayesian optimization, EngOpt, 2026, Volume 422, 2026, pp 02009
DOI：10.1051/matecconf/202642202009
