对离散空间的基于模拟的优化,使用投射到连续的潜空间
Gabriel Hernández-Morales1,2, Brenda Cansino-Loeza1, Arturo Jiménez-Gutiérrez2
1Department of Chemical and Biological Engineering, University of Wisconsin - Madison, 1415 Engineering Dr, Madison, Wisconsin 53706, United States.
Industrial & engineering chemistry research
|March 2, 2026
概括
本研究介绍了一种使用变量自动编码器的新方法,通过将离散决策空间转换为连续潜伏空间来优化复杂系统. 这种方法显著降低了系统设计模拟的计算成本.
科学领域:
- 计算化学计算化学
- 工艺系统工程 工艺系统工程
- 机器学习 机器学习
背景情况:
- 具有离散决策空间的复杂系统的基于模拟的优化,由于组合式爆炸,提出了重大的计算挑战.
- 现有的方法与大量可能的替代方案作斗争,使得详尽的模拟无法实现.
- 为了设计复杂的化学过程,有效地导航这些离散空间至关重要.
研究的目的:
- 开发一种新的计算方法,以优化具有离散决策空间的复杂系统.
- 为了减少与基于模拟的优化相关的计算负担.
- 为了使化学工艺工程复杂的设计空间的有效导航.
主要方法:
- 使用变量自编码器 (VAE) 将离散决策空间转换为连续隐藏空间.
- 将VAE与贝叶斯优化 (BO) 集成在一起,以有效地探索潜在空间.
- 拟议方法应用于设计复杂的蒸系统.
主要成果:
- 拟议的基于VAE的转换有效地将离散的决策空间投射到连续的潜在空间中.
- 潜在空间的贝叶斯优化显著减少了所需的昂贵模拟的数量.
- 在设计蒸系统的案例研究中证明了成功的应用,包括酸回收和提取分隔墙柱.
结论:
- 变量自动编码器和贝叶斯优化的集成为基于模拟优化具有离散决策空间的复杂系统提供了强大而高效的解决方案.
- 这种方法显著减轻了计算挑战,为改进化学过程设计铺平了道路.
- 该方法在设计具有挑战性的分离系统时显示出实际实用性,突出了其在工艺工程中更广泛应用的潜力.
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