外观
论文arXiv 2610.1692916 天前
Digital Twins中的决策：优化、控制与学习
原文链接：Decision-Making in Digital Twins: Optimization, Control, and Learning（arXiv 2610.16929 · 发布日期：2026-10-12）
Decision-Making in Digital Twins: Optimization, Control, and Learning
本文综述Digital Twins驱动的决策方法，聚焦仿真优化、序贯决策与风险感知决策。
核心要点
- Digital Twins与物理系统双向交互，给基于仿真的优化、控制和学习带来新挑战。
- 仿真优化部分讨论输入不确定性、流式数据、决策依赖观测及资源受限等问题。
- 序贯决策部分梳理Digital Twins与随机最优控制、强化学习及贝叶斯更新方法的联系。
- 风险感知部分讨论分布鲁棒与贝叶斯风险表述，并指出研究挑战与机会。
验证范围与局限：综述论文，摘要未描述具体仿真或物理系统验证。
英文摘要 (Abstract)
A digital twin is a virtual representation of a physical system, continuously updated with data from the physical system, enabling dynamic information exchange and decision-making. This bidirectional interaction, which distinguishes digital twins from conventional simulation models, introduces new challenges to simulation-based optimization, control, and learning. This paper reviews recent developments in decision-making methodologies motivated by digital twins. We focus on three interconnected areas: simulation optimization, sequential decision making, and risk-aware decision making. For simulation optimization, we discuss data-driven simulation optimization under input uncertainty, streaming input data, decision-dependent observations, and limited computational and data-collection resources. For sequential decision making, we review the connections among digital twins, stochastic optimal control, and reinforcement learning, with emphasis on Bayesian approaches that update models and policies as new information becomes available. For risk-aware decision making, we discuss distributionally robust and Bayesian risk formulations as alternative approaches for addressing model uncertainty. Throughout the paper, we emphasize the common problem of making decisions while the underlying model is being updated. We conclude by identifying research challenges and opportunities.
作者：Enlu Zhou
来源
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