arXiv：cs.LG· Keilung Choy, Wei Xie·· 4 小时前AI 评分23
随机多尺度生物过程数字孪生的伴随校准与最优控制
Adjoint-Based Calibration and Optimal Control of Stochastic Multiscale Bioprocess Digital Twins
AI 导读
研究提出面向生物系统之系统（Bio-SoS）的偏差感知数字孪生校准与控制框架，用随机微分方程（SDE）模型建模，结合拟似然估计与伴随灵敏度分析从稀疏离散观测中校准。
正文
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 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.
| Comments: | 37 pages, 8 figures |
| Subjects: | Machine Learning (stat.ML); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.09505 [stat.ML] |
| (or arXiv:2610.09505v1 [stat.ML] for this version) | |
| https://doi.org/10.48550/arXiv.2610.09505 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Wei Xie [view email]
[v1]
Wed, 7 Oct 2026 06:03:53 UTC (6,604 KB)
来源：arXiv：cs.LG · arxiv.org