Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational models

Understand

Machine learning techniques typically rely on large datasets to create accurate classifiers.

• However, there are situations when data is scarce and expensive to acquire.
• This is the case of studies that rely on state-of-the-art computational models which typically take days to run, thus hindering the potential of machine learning tools.
• In this work, we present a novel classifier that takes advantage of lower fidelity models and inexpensive approximations to predict the binary output of expensive computer simulations.