PNNL

Abstract

Operator learning for complex, nonlinear, operators, is increasingly common in modeling physical systems. However, training machine learning methods to learn such operators requires a large amount of expensive, high-fidelity data. In this work we present a composite Deep Operator Network (DeepONets) for learning using two datasets with different levels of fidelity, to accurately learn complex operators when sufficient high-fidelity data is not available. Additionally, we demonstrate that the presence of low-fidelity data can improve the predictions of physics-informed learning with DeepONets.