Accurate predictive modeling plays a crucial role in optimizing system performance, preventing failures, and refining maintenance strategies. While traditional modeling techniques, particularly black-box models, offer point predictions, the demand for predictions accompanied by measurable confidence has grown, particularly in high-risk scenarios. The Conformal Prediction (CP) framework provides a means to establish an error rate bound for predictions. Inductive conformal prediction necessitates calibration sets with a sufficient number of instances to uphold the chosen confidence level. This requirement entails a separate set of instances, referred to as the calibration set, which was not utilized during model training. Such a demand presents a challenge, particularly for intricate systems that are expensive to evaluate and have limited datasets. In our study, we introduce a novel framework that seamlessly integrates surrogate modeling with conformal prediction to effectively tackle these obstacles. Our method constructs a surrogate model to interpolate the calibration set and p-value. This approach significantly enhances the model's prediction capacity, facilitating more informed decision-making. To validate the efficacy of our proposed method, we apply it to a numerical example. The results underscore its capability to reduce dependency on large sample size, pushing the boundaries of the field toward more efficient and precise uncertainty quantification.
Yanwen Xu (Tue,) studied this question.