
The author argues that Physics-Informed Neural Networks (PINNs) with skip connections enhance modeling accuracy for gas-lifted oil wells, improving gradient flow and control predictions.


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Physics-Informed-Neural-Networks-with-Skip-Connections-for-Modeling-and-Control-of-Gas-Lifted-Oil-Wells


Physics-Informed Neural Networks with Skip Connections for Modeling and Control of Gas-Lifted Oil Wells


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The author introduces the Hard Constrained Sequential PINN (HCS-PINN) method to enforce temporal continuity between neural network segments, eliminating the need for additional loss terms.


exact-enforcement-of-temporal-continuity-in-sequential-physics-informed-neural-networks


Exact Enforcement of Temporal Continuity in Sequential Physics-Informed Neural Networks
