Showing math.OCShow all
3 papers · 1 filter
math.OC2026
PINNs in PDE Constrained Optimal Control Problems: Direct vs Indirect Methods
Zhen Zhang, Shanqing Liu, Alessandro Alla +2
We study physics-informed neural networks (PINNs) as numerical tools for the optimal control of semilinear partial differential equations. We first recall the classical direct and…
math.OC2024
A PINN approach for the online identification and control of unknown PDEs
Alessandro Alla, Giulia Bertaglia, Elisa Calzola
Physics-Informed Neural Networks (PINNs) have revolutionized solving differential equations by integrating physical laws into neural networks training. This paper explores PINNs fo…
math.OC2024
A POD approach to identify and control PDEs online through State Dependent Riccati equations
Alessandro Alla, Agnese Pacifico
We address the control of Partial Differential equations (PDEs) with unknown parameters. Our objective is to devise an efficient algorithm capable of both identifying and controlli…