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.
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.