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Uncertainty in safety-critical cyber-physical systems can be modeled using a finite number of parameters or parameterized input signals. Given a system specification in Signal Temporal Logic (STL), we would like to verify that for all (infinite) values of the model parameters/input signals, the system satisfies its specification. Unfortunately, this problem is undecidable in general.Statistical model checking(SMC) offers a solution by providing guarantees on the correctness of CPS models by statistically reasoning on model simulations. We propose a new approach for statistical verification of CPS models for user-provided distribution on the model parameters. Our technique uses model simulations to learnsurrogate models, and usesconformal inferenceto provide probabilistic guarantees on the satisfaction of a given STL property. Additionally, we can provide prediction intervals containing the quantitative satisfaction values of the given STL property for any user-specified confidence level. We compare this prediction interval with the interval we get using risk estimation procedures. We also propose a refinement procedure based on Gaussian Process (GP)-based surrogate models for obtaining fine-grained probabilistic guarantees over sub-regions in the parameter space. This in turn enables the CPS designer to choose assured validity domains in the parameter space for safety-critical applications. Finally, we demonstrate the efficacy of our technique on several CPS models. 

In this paper, we consider the problem of monitoring temporal patterns expressed in Signal Temporal Logic (STL) over time-series data in a clairvoyant fashion. Existing offline or online monitoring algorithms can only compute the satisfaction of a given STL formula on the time-series data that is available. We use off-the-shelf statistical time-series analysis techniques to fit available data to a model and use this model to forecast future signal values. We derive the joint probability distribution of predicted signal values and use this to compute the satisfaction probability of a given signal pattern over the prediction horizon. There are numerous potential applications of such prescient detection of temporal patterns. We demonstrate practicality of our approach on case studies in automated insulin delivery, unmanned aerial vehicles, and household power consumption data.