42 citations · 52 across the 5 of their papers we have counts for
5 papers
Challenges and Advancements in Modeling Shock Fronts with Physics-Informed Neural Networks: A Review and Benchmarking Study
Jassem Abbasi, Ameya D. Jagtap, Ben Moseley +2
Solving partial differential equations (PDEs) with discontinuous solutions , such as shock waves in multiphase viscous flow in porous media , is critical for a wide range of scient…
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…
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…
Early- and late-time prediction of counter-current spontaneous imbibition, scaling analysis and estimation of the capillary diffusion coefficient
Pål Østebø Andersen
Solutions are investigated for 1D linear counter-current spontaneous imbibition (COUSI). The diffusion problem is scaled to depend only on a normalized coefficient Λ_n (S_n ) with…
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…