Estimating Inconsistency Response Surfaces under Uncertainty in Cyber-Physical System Development

2026-09-10

AI Summary

本文通过干预响应建模及多保真蒙特卡洛估计方法，解决了在不确定性条件下网络物理系统开发中的一致性问题，并能有效分析和修复不一致性。

Abstract

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.

Problem

• Cyber-Physical Systems
• Uncertainty
• Inconsistency
• Model Compatibility
• Consistency Management

Innovation

• intervention response modeling
• Saltelli sampling
• multi-fidelity Monte Carlo estimation
• surrogate model
• gradient-based consistency recourse