基于数值优化的激光等离子体加速器电子源设计的代理模型研究
Surrogate Models studies for laser-plasma accelerator electron source design through numerical optimisation
AI总结：
本研究利用机器学习构建代理模型，基于3000多次PIC模拟，快速预测激光等离子体注入器束流参数，实现高效优化设计。
摘要：
设计由激光束驱动的高质量等离子体注入器电子源依赖于使用粒子网格（PIC）代码的数值参数研究。常见的探索输入参数包括激光特性、等离子体种类以及由计算流体动力学研究产生的密度分布。我们展示了基于机器学习技术为激光等离子体注入器（LPI）构建代理模型的方法，该模型基于Drobniak [Phys. Rev. Accel. Beams, 26, 091302, (2023)] 发表的针对稀疏间隔输入参数进行的超过3000次激光尾场加速粒子网格模拟。代理模型对于LPI设计和优化具有重要意义，因为它们比PIC模拟快约$10^7$倍。其速度使得通过广泛探索输入-输出关系而无需显著计算成本，从而实现更高效的设计研究。我们开发并比较了三种代理模型的性能，即多层感知器（MLP）、决策树（DT）和高斯过程（GP）。我们表明，使用一个简单且节俭的基于MLP的模型，在合理的随机扫描数据集（包含500个粒子网格模拟）上训练，可以以决定系数得分$R^2=0.93$预测束流参数。最佳代理模型用于快速找到最优工作点和稳定区域，并通过不同方法（即随机搜索、贝叶斯优化和多目标贝叶斯优化）获得目标电子束能量、电荷、能散和发射度。这种简单方法可以服务于激光驱动线性加速器中LPI的从始至终更全局的设计研究，针对束流能量、电荷和能散使用不同方法，即随机搜索、贝叶斯优化和多目标贝叶斯优化。
英文摘要：
Designing a high-quality plasma injector electron source driven by a laser beam relies on numerical parametric studies using particle-in-cell codes. The common input parameters to explore are laser characteristics, plasma species and density profiles produced by computational fluid dynamic studies. We demonstrate the construction of surrogate models using machine learning techniques for a laser-plasma injector (LPI) based on more than $3000$ particle-in-cell simulations of laser wakefield acceleration performed for sparsely spaced input parameters published by Drobniak [Phys. Rev. Accel. Beams, 26, 091302, (2023)]. Surrogate models are relevant for LPI design and optimisation, as they are approximately $10^7$ times faster than PIC simulations. Their speed enables more efficient design studies by allowing extensive exploration of the input-output relationship without significant computational cost. We develop and compare the performance of three surrogate models, namely, multilayer perceptron (MLP), decision trees (DT) and Gaussian processes (GP). We show that using a simple and frugal MLP-based model trained on a reasonable-size random scan data set of 500 particles in cell simulations, we can predict beam parameters with a coefficient determination score $R^2=0.93$ . The best surrogate model is used to quickly find optimal working points and stability regions and get targeted electron beam energy, charge, energy spread and emittance using different methods, namely random search, Bayesian optimisation and multi-objective Bayesian optimisation. This simple approach can serve more global design study of an LPI in a start-to-end linear laser-driven accelerator.on beam energy, charge and energy spread using different methods, namely random search, Bayesian optimisation and multi-objective Bayesian optimisation
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