仿真模型与数字孪生的子轨迹条件验证
Subtrace-Conditional Validation of Simulation Models and Digital Twins
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中文总结 AI 辅助
研究针对仿真模型验证问题，提出统计验证框架，通过固定部分随机原语获取条件输出分布用于拟合优度测试，并开发诊断工具，经实验证明能检测现有方法可能遗漏的输入模型错误设定。
中文摘要 AI 辅助
针对历史输出数据验证仿真模型对其在数字孪生环境中的成功部署至关重要。我们提出一个统计验证框架，从观察到的系统状态反复初始化仿真模型，固定随机输入模型子集中的随机原语到其观察到的实现，同时模拟其余原语来获得条件输出分布，用于拟合优度测试以验证仿真模型。还开发诊断工具识别对仿真模型输出与现实间偏差贡献最大的输入模型。在M/M/1排队系统和串联排队系统的数字孪生仿真上的数值实验表明，该框架能检测现有仅验证边际输出分布的方法可能遗漏的输入模型错误设定。
英文摘要
Validating simulation models against historical output data is essential for their successful deployment in digital-twin environments. We propose a statistical validation framework in which a simulation model is repeatedly initialized from observed system states, and conditional output distributions are obtained by fixing the random primitives from a subset of stochastic input models to their observed realizations while simulating the remaining primitives. These conditional output distributions are then used in goodness-of-fit tests to validate the simulation model with respect to combinations of input models. We also develop diagnostic tools to identify the input models that most contribute to any observed misalignment between a simulation model's outputs and reality. Numerical experiments on an M/M/1 queueing system and a digital-twin-enabled simulation of a tandem queueing system demonstrate that the proposed framework can detect misspecifications in input models that may be missed by existing approaches that validate only the marginal output distribution.
