62 citations · 70 across the 2 of their papers we have counts for
3 papers
cs.LG2021★ 8 cited
Improved architectures and training algorithms for deep operator networks
Sifan Wang, Hanwen Wang, Paris Perdikaris
Operator learning techniques have recently emerged as a powerful tool for learning maps between infinite-dimensional Banach spaces. Trained under appropriate constraints, they can…
cs.LG2021★ 62 cited
Learning the solution operator of parametric partial differential equations with physics-informed DeepOnets
Sifan Wang, Hanwen Wang, Paris Perdikaris
Deep operator networks (DeepONets) are receiving increased attention thanks to their demonstrated capability to approximate nonlinear operators between infinite-dimensional Banach…
cs.LG2020
On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks
Sifan Wang, Hanwen Wang, Paris Perdikaris
Physics-informed neural networks (PINNs) are demonstrating remarkable promise in integrating physical models with gappy and noisy observational data, but they still struggle in cas…