1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.LG2026
Solving the Elastic Wave Equation with Physics-Informed Neural Networks: A Robust and Critical Assessment
Davide Staub, Ben Moseley
Physics-Informed Neural Networks (PINNs) have recently emerged as a promising approach for solving Partial Differential Equations (PDEs), offering a meshfree alternative that integ…
physics.flu-dyn2025
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…
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…