English(EN) Adjoint-Based Calibration and Optimal Control of Stochastic Multiscale Bioprocess Digital Twins

新框架增强生物过程数字孪生校准与控制

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-08 04:00

研究人员开发了一种用于多尺度生物过程数字孪生校准与控制的新框架。该框架利用随机微分方程和拟似然估计，并结合伴随敏感性分析来管理参数偏差和不确定性。引入了Actor-Simulator算法来联合优化模型参数、选择实验和改进控制策略，证明了其在准确性和效率方面优于现有方法。 AI

影响这项研究可能带来更准确、更高效的生物过程数字孪生，对药物开发和制造产生影响。

排序理由该集群包含一篇详细介绍生物过程数字孪生新框架的研究论文。[lever_c_demoted from research: ic=1 ai=0.7]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.LG TIER_1 English(EN) · Keilung Choy, Wei Xie · 2026-10-08 04:00

随机多尺度生物过程数字孪生的伴随法校准与最优控制

arXiv:2610.09505v1 Announce Type: cross Abstract: 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 equati…