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This literature survey discusses various applications and advancements of Physics-Informed Neural Networks (PINNs) for solving first-order processes with proportional control. Key findings include the effectiveness of FO-PINNs in reducing validation errors and accurately reconstructing pressure from sparse data, as well as the successful integration of PINNs in nonlinear model predictive control scenarios. The document synthesizes results across multiple studies, highlighting improvements in accuracy, speed

Author

aminhaj774

Language

EN