Scholarlink

<b>Purpose</b> <p>In the field of accelerators, identifying optimized operational points and ideal solutions for complex systems remains a significant challenge due to the large number of parameters and intricate nonlinear dynamics involved.</p> <b>Methods</b> <p>In this study, we present two diffraction-limited storage ring (DLSR) lattice models based on deep and invertible neural networks (INNs), each incorporating both forward and inverse models. These models play a crucial role in enhancing multi-objective evolutionary algorithms (EAs) and expanding the set of viable solutions.</p> <b>Results and Conclusion</b> <p>We evaluate the accuracy of both the forward and inverse models of two lattice configurations; the accuracy is as high as 97%. Explore the Pareto-optimal trade-offs between emittance and dynamic aperture. INNs were applied in DLSR lattices for the first time, and lattices with natural emittances of 114 pm<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\cdot \)</EquationSource> <EquationSource Format="MATHML"><math> <mo>·</mo> </math></EquationSource> </InlineEquation>rad and 23 pm<InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\cdot \)</EquationSource> <EquationSource Format="MATHML"><math> <mo>·</mo> </math></EquationSource> </InlineEquation>rad were obtained at energies of 2 GeV and 6 GeV, respectively, both with reasonable dynamic aperture. Additionally, the use of invertible neural networks significantly reduces computational costs and time requirements. This study provides a valuable reference for future research in multi-objective optimization for lattice design.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Design and optimization of diffraction-limited storage ring lattices based on invertible neural networks

  • 期刊论文

  • 发表日期：

    2025年6月24日

查看全文
  • Cun-Yang Que,
  • He-Xin Yin,
  • Jia-Bao Guan,
  • Ye Zou,
  • Ji-Ke Wang

摘要

Purpose

In the field of accelerators, identifying optimized operational points and ideal solutions for complex systems remains a significant challenge due to the large number of parameters and intricate nonlinear dynamics involved.

Methods

In this study, we present two diffraction-limited storage ring (DLSR) lattice models based on deep and invertible neural networks (INNs), each incorporating both forward and inverse models. These models play a crucial role in enhancing multi-objective evolutionary algorithms (EAs) and expanding the set of viable solutions.

Results and Conclusion

We evaluate the accuracy of both the forward and inverse models of two lattice configurations; the accuracy is as high as 97%. Explore the Pareto-optimal trade-offs between emittance and dynamic aperture. INNs were applied in DLSR lattices for the first time, and lattices with natural emittances of 114 pm \(\cdot \) · rad and 23 pm \(\cdot \) · rad were obtained at energies of 2 GeV and 6 GeV, respectively, both with reasonable dynamic aperture. Additionally, the use of invertible neural networks significantly reduces computational costs and time requirements. This study provides a valuable reference for future research in multi-objective optimization for lattice design.