8 citations · 10 across the 4 of their papers we have counts for
4 papers
High-fidelity Multiphysics Modelling for Rapid Predictions Using Physics-informed Parallel Neural Operator
Biao Yuan, He Wang, Yanjie Song +2
Modelling complex multiphysics systems governed by nonlinear and strongly coupled partial differential equations (PDEs) is a cornerstone in computational science and engineering. H…
Physics-informed Deep Learning to Solve Three-dimensional Terzaghi Consolidation Equation: Forward and Inverse Problems
Biao Yuan, Ana Heitor, He Wang +1
The emergence of neural networks constrained by physical governing equations has sparked a new trend in deep learning research, which is known as Physics-Informed Neural Networks (…
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…