用多点贝叶斯算法大幅降低加速器设计计算成本，提升性能优化效率。
Efficient Dynamic and Momentum Aperture Optimization for Lattice Design Using Multipoint Bayesian Algorithm Execution
- 采用单粒子级多点贝叶斯算法，同时优化动态和动量区，替代传统高耗时仿真。
- 相比遗传算法，计算量减少超200倍，仍能达到相当的帕累托最优结果。
- 适用于第四代光源、对撞机等大型科学装置的高效设计，适合加速器物理研究者。
我们证明，多点贝叶斯算法执行可克服储存环设计优化中的根本计算挑战。动态区（DA）与动量区（MA）优化是储存环的多目标、多点设计任务，直接影响同步辐射源通量与对撞机亮度。现有黑箱优化方法需对每个候选配置进行大量粒子追踪模拟，计算开销大，搜索范围受限于约10³个配置，制约了最终设计质量。本文提出multipointBAX，通过在单粒子层面选择、模拟并建模每个候选配置，突破此瓶颈。我们在新一代第四代光源设计中验证该方法，基于神经网络的multipointBAX在追踪计算量减少超过两个数量级的前提下，实现了与遗传算法相当的帕累托前沿结果。该显著降本使multipointBAX成为黑箱优化的有力替代方案，有望在下一代光源、对撞机及大型科学设施设计中发挥关键作用。
原文摘要 · Abstract (English)
We demonstrate that multipoint Bayesian algorithm execution can overcome fundamental computational challenges in storage ring design optimization. Dynamic (DA) and momentum (MA) optimization is a multipoint, multiobjective design task for storage rings, ultimately informing the flux of x-ray sources and luminosity of colliders. Current state-of-art black-box optimization methods require extensive particle-tracking simulations for each trial configuration; the high computational cost restricts the extent of the search to $\sim 10^3$ configurations, and therefore limits the quality of the final design. We remove this bottleneck using multipointBAX, which selects, simulates, and models each trial configuration at the single particle level. We demonstrate our approach on a novel design for a fourth-generation light source, with neural-network powered multipointBAX achieving equivalent Pareto front results using more than two orders of magnitude fewer tracking computations compared to genetic algorithms. The significant reduction in cost positions multipointBAX as a promising alternative to black-box optimization, and we anticipate multipointBAX will be instrumental in the design of future light sources, colliders, and large-scale scientific facilities.
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