Driven by the critical challenges in biomanufacturing, including high complexity and high uncertainty, we propose a comprehensive and computationally efficient sensitivity analysis framework for general nonlinear policy-augmented knowledge graphical (pKG) hybrid models that characterize the risk- and science-based understandings of underlying stochastic decision process mechanisms. The criticality of each input (i.e., random factors, policy parameters, and model parameters) is measured by applying Shapley value (SV) sensitivity analysis to pKG (called SV-pKG), accounting for process causal interdependences. To quickly assess the SV for heavily instrumented bioprocesses, we approximate their dynamics with linear Gaussian pKG models and improve the SV estimation efficiency by utilizing the linear Gaussian properties. In addition, we propose an effective permutation sampling method with TFWW transformation and variance reduction techniques, namely the quasi-Monte Carlo and antithetic sampling methods, to further improve the sampling efficiency and estimation accuracy of SV for both general nonlinear and linear Gaussian pKG models. Our proposed framework can benefit efficient interpretation and support stable optimal process control in biomanufacturing.

翻译：受生物制造中高度复杂性和高度不确定性等关键挑战的驱动，我们为通用的非线性政策增强知识图混合模型提出了一个全面且计算高效的敏感性分析框架。该框架旨在刻画对底层随机决策过程机制基于风险与科学的理解。通过将沙普利值敏感性分析应用于pKG模型，我们衡量了每个输入变量的关键性，该方法被称为SV-pKG，并考虑了过程间的因果依赖关系。为了快速评估高度仪器化生物过程的SV，我们使用线性高斯pKG模型来近似其动态，并利用线性高斯特性提高了SV估计效率。此外，我们提出了一种有效的排列抽样方法，结合TFWW变换及方差缩减技术，即拟蒙特卡洛和对偶抽样方法，以进一步提高通用非线性及线性高斯pKG模型的SV抽样效率与估计精度。我们提出的框架有助于实现高效解释并支持生物制造中稳定的最优过程控制。