Optimization and Control

Learning the Riccati solution operator for time-varying LQR via Deep Operator Networks

This paper uses Deep Operator Networks (DeepONets) to learn a surrogate for the Riccati differential equation solution operator in finite-horizon LQR problems, enabling fast approximate optimal control without repeated numerical integration. The approach includes theoretical guarantees on stability and performance, and demonstrates significant computational speedups while maintaining high accuracy.