45 citations · 167 across the 14 of their papers we have counts for
15 papers
Hard Constraint Projection in a Physics Informed Neural Network
Miranda J. S. Horne, Peter K. Jimack, Amirul Khan +1
In this work, we embed hard constraints in a physics informed neural network (PINN) which predicts solutions to the 2D incompressible Navier Stokes equations. We extend the hard co…
Generative modeling of Sparse Approximate Inverse Preconditioners
Mou Li, He Wang, Peter K. Jimack
We present a new deep learning paradigm for the generation of sparse approximate inverse (SPAI) preconditioners for matrix systems arising from the mesh-based discretization of ell…
Investigating Guiding Information for Adaptive Collocation Point Sampling in PINNs
Jose Florido, He Wang, Amirul Khan +1
Physics-informed neural networks (PINNs) provide a means of obtaining approximate solutions of partial differential equations and systems through the minimisation of an objective f…
Understanding the Efficacy of U-Net & Vision Transformer for Groundwater Numerical Modelling
Maria Luisa Taccari, Oded Ovadia, He Wang +3
This paper presents a comprehensive comparison of various machine learning models, namely U-Net, U-Net integrated with Vision Transformers (ViT), and Fourier Neural Operator (FNO),…
Developing a cost-effective emulator for groundwater flow modeling using deep neural operators
Maria Luisa Taccari, He Wang, Somdatta Goswami +3
Current groundwater models face a significant challenge in their implementation due to heavy computational burdens. To overcome this, our work proposes a cost-effective emulator th…
Attention U-Net as a surrogate model for groundwater prediction
Maria Luisa Taccari, Jonathan Nuttall, Xiaohui Chen +3
Numerical simulations of groundwater flow are used to analyze and predict the response of an aquifer system to its change in state by approximating the solution of the fundamental…