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arXiv Machine Learning By Johannes M\"akelburg, Tim Schwabe, Maribel Acosta

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

Read the original on arXiv Machine Learning →

The paper presents a method to estimate inconsistency response surfaces in Cyber‑Physical Systems (CPS) under uncertainty. By reformulating inconsistency as an intervention‑response modeling problem, the authors use Saltelli sampling and multi‑fidelity Monte Carlo estimation to generate datasets, then train a surrogate model that predicts inconsistency from propagated uncertainty geometry. Experiments on 48 scenarios across 10 CPS domains show that the surrogate matches Monte Carlo estimates while dramatically reducing evaluation time, enabling extensive sensitivity analysis and a gradient‑based consistency recourse method to identify minimal interventions that restore consistency.

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