Multifidelity Deep Operator Networks with Applications to Ice Sheet Modeling

  • Howard, Amanda (Pacific Northwest National Laboratory)
  • Perego, Mauro (Sandia National Laboratories)
  • Karniadakis, George (Brown University)
  • Stinis, Panos (Pacific Northwest National Laboratory)

Please login to view abstract download link

Operator learning for complex nonlinear systems is increasingly common in modeling multi-physics and multi-scale systems such as climate modeling. However, training such high-dimensional operators requires a large amount of expensive, high-fidelity data, either from experiments or simulations. In this work, we present a composite Deep Operator Network (DeepONet) 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. We demonstrate the new multi-fidelity training in diverse examples, including modeling of the ice-sheet dynamics of the Humboldt glacier, Greenland, using two different fidelity models and also using the same physical model at two different resolutions.