42 citations · 52 across the 3 of their papers we have counts for
3 papers
cs.CE2024★ 1 cited
History-Matching of Imbibition Flow in Multiscale Fractured Porous Media Using Physics-Informed Neural Networks (PINNs)
Jassem Abbasi, Ben Moseley, Takeshi Kurotori +4
We propose a workflow based on physics-informed neural networks (PINNs) to model multiphase fluid flow in fractured porous media. After validating the workflow in forward and inver…
physics.comp-ph2023★ 9 cited
Simulation and Prediction of Countercurrent Spontaneous Imbibition at Early and Late Times Using Physics-Informed Neural Networks
Jassem Abbasi, Pål Østebø Andersen
The application of Physics-Informed Neural Networks (PINNs) is investigated for the first time in solving the one-dimensional Countercurrent spontaneous imbibition (COUCSI) problem…
cs.LG2022★ 42 cited
Physical Activation Functions (PAFs): An Approach for More Efficient Induction of Physics into Physics-Informed Neural Networks (PINNs)
Jassem Abbasi, Pål Østebø Andersen
In recent years, the gap between Deep Learning (DL) methods and analytical or numerical approaches in scientific computing is tried to be filled by the evolution of Physics-Informe…