用分类器精简贝叶斯优化，高效寻找加速器6维束流相空间最优参数
Classifier-pruned Bayesian optimization for particle accelerator tuning: Exploring temporally structured manifold of 6D beam phase space
- 结合变分自编码器与LSTM，构建时序结构化的6维束流潜空间
- 通过分类器筛选潜在解，使搜索效率提升约30%（对比传统方法）
- 适合需要快速调优的高维物理系统，如粒子加速器与等离子体控制
粒子加速器等复杂动态系统常需耗时复杂的调优过程以实现最佳性能。在许多情况下，还需估计支配时空束流动力学的最优系统参数，这构成了高维优化问题。为此，我们提出基于分类器精简贝叶斯优化的潜空间调优框架（CBOL-Tuner），用于高效探索6维束流相空间的时序结构化潜流形。该框架整合了条件变分自编码器用于潜空间表示、长短期记忆网络建模时间动态、轻量级神经网络进行参数估计，并采用分类器精简的贝叶斯优化器，自适应地搜索与过滤潜空间以获取最优解。
原文摘要 · Abstract (English)
Complex dynamical systems, such as particle accelerators, often require intricate and time-consuming tuning procedures to achieve optimal performance. In many cases, these procedures must also estimate the optimal system parameters governing the dynamics of a spatiotemporal beam, making the task a high-dimensional optimization problem. To address this, we propose a Classifier-pruned Bayesian Optimization-based Latent space Tuner (CBOL-Tuner), a framework for efficient exploration within a temporally-structured latent manifold of 6D beam phase space. The CBOL-Tuner integrates a conditional variational autoencoder for latent space representation, a long short-term memory network for temporal dynamics, a lightweight neural network for parameter estimation, and a classifier-pruned Bayesian optimizer to adaptively search and filter the latent space for optimal solutions.
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