126 citations · 403 across the 35 of their papers we have counts for
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physics.flu-dyn2024★ 2 cited
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.flu-dyn2022★ 72 cited
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…