2022/05/06 by Jostein Barry-Straume, Arash Sarshar, Barry-Straume, Jostein +5 · 1 voice · 1 citation
Computer Science · Mathematics · #FOS: Computer and information sciences #FOS: Mathematics #G.1.6 #G.1.8 #I.2.6 #I.2.8 #I.5.1 #Machine Learning (cs.LG) #Optimization and Control (math.OC) #cs.LG #math.OC
paper · pdf · doi:10.48550/arxiv.2205.03377
arxiv published 2022/05/06 · arxiv updated 2022/08/18
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. The success of Control PINNs is demonstrated by solving the following open-loop optimal control problems: (i) an analytical problem, (ii) a one-dimensional heat equation, and (iii) a two-dimensional predator-prey problem.