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...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025015099 |
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