Deep neural operators as accurate surrogates for shape optimization

Khemraj Shukla, Vivek Oommen, Ahmad Peyvan, Michael Penwarden, Nicholas Plewacki, Luis Bravo, Anindya Ghoshal, Robert M. Kirby, George Em Karniadakis

Abstract: Highlights•Generating an efficient and inexpensive surrogate model based on DeepONets.•The surrogate model is invariant to the low or high-dimensional parameterizations.•Prediction of flow field is used for various cost functions in the optimization loop.•Computing drag to lift ratio using the inferred high-dimensional flow field.•Integration of the Dakota optimization framework with the DeepONet surrogate.