Conformal Prediction on Quantifying Uncertainty of Dynamic Systems
This work addresses the lack of systematic uncertainty assessment in physical data for AI reliability, though it appears incremental as it applies an existing method (conformal prediction) to a specific domain.
The paper tackles the problem of quantifying uncertainty in dynamic systems from video data by introducing conformal prediction, which provides theoretical guarantees and effectively evaluates uncertainty in time-series tasks using benchmark operator learning methods.
Numerous studies have focused on learning and understanding the dynamics of physical systems from video data, such as spatial intelligence. Artificial intelligence requires quantitative assessments of the uncertainty of the model to ensure reliability. However, there is still a relative lack of systematic assessment of the uncertainties, particularly the uncertainties of the physical data. Our motivation is to introduce conformal prediction into the uncertainty assessment of dynamical systems, providing a method supported by theoretical guarantees. This paper uses the conformal prediction method to assess uncertainties with benchmark operator learning methods. We have also compared the Monte Carlo Dropout and Ensemble methods in the partial differential equations dataset, effectively evaluating uncertainty through straight roll-outs, making it ideal for time-series tasks.