118 citations · 334 across the 18 of their papers we have counts for
3 papers · 1 filter
Neural Operator Learning for Ultrasound Tomography Inversion
Haocheng Dai, Michael Penwarden, Robert M. Kirby +1
Neural operator learning as a means of mapping between complex function spaces has garnered significant attention in the field of computational science and engineering (CS&E). In t…
Deep neural operators can serve as accurate surrogates for shape optimization: A case study for airfoils
Khemraj Shukla, Vivek Oommen, Ahmad Peyvan +5
Deep neural operators, such as DeepONets, have changed the paradigm in high-dimensional nonlinear regression from function regression to (differential) operator regression, paving…
A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositions
Michael Penwarden, Ameya D. Jagtap, Shandian Zhe +2
Physics-informed neural networks (PINNs) as a means of solving partial differential equations (PDE) have garnered much attention in the Computational Science and Engineering (CS&E)…