Multi-Fidelity Surrogate Modelling for Uncertainty-Aware Prediction of Disturbed Velocity Fields in Virtual Flow Metering
This paper presents an auto-regressive multi-fidelity Gaussian process regression framework that fuses sparse, high-fidelity LDV measurements with abundant, biased CFD simulations to create a real-time, uncertainty-aware surrogate model for predicting disturbed velocity fields in virtual flow metering, achieving significant error reduction and reliable 95% prediction intervals.