72 citations · 99 across the 9 of their papers we have counts for
3 papers · 1 filter
Physics-Informed Neural Networks for Multi-Phase Flow in Porous Media Considering Dual Shocks and Interphase Solubility
Jingjing Zhang, Ulisses Braga-Neto, Eduardo Gildin
Physics-Informed Neural Networks (PINNs) integrate physical principles into machine learning, finding wide applications in various science and engineering fields. However, solving…
Physics-Informed Neural Network for the Transient Diffusivity Equation in Reservoir Engineering
Daniel Badawi, Eduardo Gildin
Physics-Informed machine learning models have recently emerged with some interesting and unique features that can be applied to reservoir engineering. In particular, physics-inform…
Physics-Informed Neural Networks with Adaptive Localized Artificial Viscosity
E. J. R. Coutinho, M. Dall'Aqua, L. McClenny +3
Physics-informed Neural Network (PINN) is a promising tool that has been applied in a variety of physical phenomena described by partial differential equations (PDE). However, it h…