染色体建模的演变:从机械模型到基于物理的深度学习的混合模型
Yu-Cheng Chen1, Zhiyuan Chen2, Shi-Peng Dai1
1Marine Biological Manufacturing Center of Fuzhou Institute of Oceanography, Fuzhou University, Fuzhou, 350108, China; Fujian Engineering and Technology Research Center for Comprehensive Utilization of Marine Products Waste, Fuzhou University, Fuzhou, 350108, China; Fuzhou Industrial Technology Innovation Center for High Value Utilization of Marine Products, Fuzhou University, Fuzhou, 350108, China.
基于物理的深度学习 (PBDL) 通过将机械学理解与数据驱动方法相结合,推进色谱建模. 本综述详细介绍了三代PBDL,增强模拟并使智能生物工艺工程成为可能.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 计算科学 计算科学
背景情况:
- 传统的染色学模型在捕捉复杂的行为方面面临限制.
- 混合建模通过结合物理和数据提供了一种新的方法.
研究的目的:
- 审查基于物理的深度学习 (PBDL) 方法在染色学中的演变.
- 为了突出PBDL在染色体建模方面的进展.
主要方法:
- 对三代PBDL的审查:基于代理模型的解决器,基于物理的神经网络和可微分的数值模拟.
- 分析神经网络在数值解决器中的集成.
主要成果:
- 第一代PBDL使用替代模型进行更快的模拟 ("数据辅助物理学").
- 第二代PBDL采用物理信息的神经网络进行集成学习 ("物理约束数据").
- 第三代PBDL使用可微分模拟进行高保真建模和优化 ("物理与数据之间的相互反").
结论:
- PBDL使染色体模型能够在物理约束下自学复杂的吸附行为.
- 这些进步为实时数字双胞胎和染色体学中智能生物过程建模铺平了道路.
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