Application of physics-informed neural networks for two-phase flow model with variable diffusion and experimental validation

Application of physics-informed neural networks for two-phase flow model with variable diffusion and experimental validation

Recent advancements in deep learning have significantly improved solving complex computational physics problems. This paper presents Physics-Informed Neural Networks (PINNs) with a spatially-dependent diffusion function to model two-phase flow in porous media, explicitly addressing the Buckley-Lever...

Full description

Saved in:
Bibliographic Details
Main Authors: Daulet Kalesh, Timur Merembayev, Sagyn Omirbekov, Yerlan Amanbek
Format: Article
Language:English
Published: Elsevier 2025-06-01
Series:Results in Engineering
Subjects:
PINNs
Two-phase flow
Buckley-Leverett
Machine learning
MLP
Attention-based
Online Access:http://www.sciencedirect.com/science/article/pii/S2590123025015099
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items