发表机构
Northeastern University（东北大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对多尺度生物过程数字孪生，提出一种偏差感知的校准与控制框架，利用伴随灵敏度分析校准SDE模型，并通过Actor-Simulator算法联合优化参数、实验和策略，提升校准精度与控制性能。
AI 中文摘要
我们开发了一个偏差感知的数字孪生校准与控制框架，用于生物系统之系统（Bio-SoS）范式下的多尺度生物过程模型。数字孪生由随机微分方程（SDE）模型表示，并利用拟似然估计和伴随灵敏度分析从稀疏、离散的观测中进行校准。基于SDE生成元的矩展开刻画了截断引起的参数偏差，而前向-后向伴随量化了校准不确定性如何传播到价值函数和策略性能。由此产生的参数误差分布支持策略导向的自适应实验设计和通过二阶高斯平均目标进行的不确定性感知策略优化。我们刻画了所提出的探索准则的渐近行为，并推导了在优化策略下的物理系统性能。为实现这些想法，我们开发了一种Actor-Simulator算法，该算法联合更新模型参数、选择信息丰富的实验并优化控制策略。数值研究表明，与最先进的基线相比，校准精度、样本效率和控制性能均有所提高。
英文摘要
We develop a bias-aware digital-twin calibration and control framework for multiscale bioprocess models within a biological systems-of-systems (Bio-SoS) paradigm. The digital twin is represented by a stochastic differential equation (SDE) model and calibrated from sparse, discrete observations using quasi-likelihood estimation and adjoint sensitivity analysis. SDE generator-based moment expansions characterize truncation-induced parameter bias, while forward-backward adjoints quantify how calibration uncertainty propagates to value functions and policy performance. The resulting parameter-error distribution supports both policy-directed adaptive experimental design and uncertainty-aware policy optimization through a second-order Gaussian-averaged objective. We characterize the asymptotic behavior of the resulting exploration criterion and derive a physical-system performance under the optimized policy. To implement these ideas, we develop an Actor-Simulator algorithm that jointly updates model parameters, selects informative experiments, and optimizes control policies. Numerical studies demonstrate improved calibration accuracy, sample efficiency, and control performance relative to state-of-the-art baselines.
Comments37 pages, 8 figures
