7 citations · 9 across the 5 of their papers we have counts for
5 papers
Temporal Neural Operator for Modeling Time-Dependent Physical Phenomena
W. Diab, M. Al-Kobaisi
Neural Operators (NOs) are machine learning models designed to solve partial differential equations (PDEs) by learning to map between function spaces. Neural Operators such as the…
Learning multi-phase flow and transport in fractured porous media with auto-regressive and recurrent graph neural networks
Mohammed Al Kobaisi, Wenjuan Zhang, Waleed Diab +1
In the past three decades, a wide array of computational methodologies and simulation frameworks has emerged to address the complexities of modeling multi-phase flow and transport…
U-DeepONet: U-Net Enhanced Deep Operator Network for Geologic Carbon Sequestration
Waleed Diab, Mohammed Al-Kobaisi
FNO and DeepONet are by far the most popular neural operator learning algorithms. FNO seems to enjoy an edge in popularity due to its ease of use, especially with high dimensional…
Learning Generic Solutions for Multiphase Transport in Porous Media via the Flux Functions Operator
Waleed Diab, Omar Chaabi, Shayma Alkobaisi +2
Traditional numerical schemes for simulating fluid flow and transport in porous media can be computationally expensive. Advances in machine learning for scientific computing have t…
PINNs for the Solution of the Hyperbolic Buckley-Leverett Problem with a Non-convex Flux Function
Waleed Diab, Mohammed Al Kobaisi
The displacement of two immiscible fluids is a common problem in fluid flow in porous media. Such a problem can be posed as a partial differential equation (PDE) in what is commonl…