Quantitative assessment of PINN inference on experimental data for gravity currents flows
In this paper, we apply physics informed neural networks (PINNs) to infer velocity and pressure field from light attenuation technique (LAT) measurements for gravity current induced by lock-exchange. In a PINN model, physical laws are embedded in the loss function of a neural network, such that the...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2025-01-01
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| Series: | Machine Learning: Science and Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2632-2153/adaca0 |
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