发表机构
Technische Universität München（慕尼黑工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出将信息物理系统开发中的不一致性建模为干预响应问题，通过Saltelli采样和多保真度蒙特卡洛估计训练代理模型，实现毫秒到微秒级的快速预测，并基于响应曲面进行敏感性分析和梯度补救以恢复一致性。
AI 中文摘要
信息物理系统（CPS）通常通过多个相互关联的模型来表示。在开发过程中，CPS的一致性要求共享的模型元素在这些模型之间保持兼容。不确定性，例如由传感器噪声或模型抽象引起的不确定性，会改变模型元素的允许值，并可能引入不一致性，即模型无法再被共同满足的情况。虽然现有方法可以确定给定不确定性配置下的一致性，但它们在系统性地探索、分析和解释大范围不确定性空间中的不一致性方面提供的支持有限。我们通过将不一致性重新表述为干预响应建模问题来解决这一挑战。利用Saltelli采样和多保真度蒙特卡洛估计，我们生成干预-响应数据集，并训练一个代理模型，该模型直接从传播的不确定性几何形状预测不一致性。在48个场景和10个CPS领域上的实验表明，该代理模型与蒙特卡洛估计相匹配，同时将评估时间从毫秒级缩短到微秒级，从而在固定的计算预算内实现了数量级更多的响应曲面评估。基于学习到的响应曲面，我们进行敏感性分析以识别主要的不确定性驱动因素，并引入一种基于梯度的一致性补救方法，以确定恢复一致性的最小不确定性干预措施。结果表明，不确定性下的不一致性可以通过响应曲面建模被有效地学习、分析和修复，为CPS开发中不确定性感知的一致性管理提供了可扩展的基础。
英文摘要
Cyber-Physical Systems (CPS) are commonly represented through multiple interconnected models. During development, CPS consistency requires that shared model elements remain compatible across these models. Uncertainty, for example, due to sensor noise or model abstraction, changes the admissible values of model elements and can introduce inconsistencies, i.e., situations in which models can no longer be jointly satisfied. While existing approaches can determine consistency for a given uncertainty configuration, they provide limited support for systematically exploring, analyzing, and explaining inconsistency across large uncertainty spaces. We address this challenge by reformulating inconsistency as an intervention response modeling problem. Using Saltelli sampling and multi-fidelity Monte Carlo estimation, we generate intervention-response datasets and train a surrogate model that directly predicts inconsistency from the propagated uncertainty geometry. Experiments on 48 scenarios and 10 CPS domains show that the surrogate matches Monte Carlo estimates while reducing evaluation time from milliseconds to microseconds, enabling orders-of-magnitude more response-surface evaluations within fixed computational budgets. Building on the learned response surfaces, we perform sensitivity analysis to identify dominant uncertainty drivers and introduce a gradient-based consistency recourse method to determine minimal uncertainty interventions that restore consistency. The results show that inconsistency under uncertainty can be effectively learned, analyzed, and repaired through response-surface modeling, providing a scalable foundation for uncertainty-aware consistency management in CPS development.
CommentsExtended version of the paper accepted at IEEE ICDM 2026; 10 pages + appendix, 11 figures, 3 tables
