Physics-informed neural networks for PDE-constrained optimization and control

Understand

A fundamental problem in science and engineering is designing optimal control policies that steer a given system towards a desired outcome.

• This work proposes Control Physics-Informed Neural Networks (Control PINNs) that simultaneously solve for a given system state, and for the optimal control signal, in a one-stage framework that conforms to the underlying physical laws.
• Prior approaches use a two-stage framework that first models and then controls a system in sequential order.
• In contrast, a Control PINN incorporates the required optimality conditions in its architecture and in its loss function.