We present a new modeling approach for the study and prediction of important process outcomes of biotechnological cultivation processes under the influence of process parameter variations. Our model is based on physics‐informed neural networks (PINNs) in combination with kinetic growth equations. Using Taylor series, multivariate external process parameter variations for important variables such as temperature, seeding cell density and feeding rates can be integrated into the corresponding kinetic rates and the governing growth equations. In addition to previous approaches, PINNs also allow continuous and differentiable functions as predictions for the process outcomes. Accordingly, our results show that PINNs in combination with Taylor‐series expansions for kinetic growth equations provide a very high prediction accuracy for important process variables such as cell densities and concentrations as well as a detailed study of individual and combined parameter influences. Furthermore, the proposed approach can also be used to evaluate the outcomes of new parameter variations and combinations, which enables a saving of experiments in combination with a model‐driven optimization study of the design space.

中文翻译：

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用于生物制药培养过程的物理信息神经网络：考虑不同的过程参数设置


我们提出了一种新的建模方法，用于研究和预测过程参数变化影响下生物技术培养过程的重要过程结果。我们的模型基于物理信息神经网络（PINN）并结合动力学增长方程。使用泰勒级数，重要变量（例如温度、接种细胞密度和补料速率）的多元外部过程参数变化可以集成到相应的动力学速率和控制生长方程中。除了以前的方法之外，PINN 还允许连续且可微的函数作为过程结果的预测。因此，我们的结果表明，PINN 与动力学生长方程的泰勒级数展开相结合，可以为细胞密度和浓度等重要过程变量提供非常高的预测精度，并可以对单个和组合参数影响进行详细研究。此外，所提出的方法还可用于评估新参数变化和组合的结果，这可以结合模型驱动的设计空间优化研究来节省实验。