Physics-Informed Neural Networks for ODE-based Dynamic Optimization in Chemical Processes

Extended prior work into a Physics-Informed Neural Network (PINN) framework modeling ODE systems of control and state vectors for dynamic optimization, achieving new best values for the batch-reactor and tubular-reactor problems (0.6109 and 0.5748) with negligible physics loss.

Accepted 2025, in press.