Tikhonov-Regularized Physics-Informed Neural Networks for Terminal-State Distributed Optimal Control of Parabolic Partial Differential Equations
In this study, we propose a Physics-Informed Neural Networks (PINNs) framework that incorporates Tikhonov regularization to solve terminal-state tracking optimal control constrained by parabolic partial differential equations (PDEs). This problem is inherently ill-posed, as infinitely many distributed controls may drive the system to the same desired state, so the regularization guides the optimizer toward the minimum-energy control, restoring numerical stability and yielding a smooth, physically meaningful solution. On the theoretical side, we establish a consistency result showing that PINNs minimizers nearly attain the continuous regularized objective under residual and quadrature approximation assumptions, and a novel error estimate that bounds the deviation of the learned control from the minimum-energy solution in terms of the PINNs training residuals and the regularization parameter. Numerical experiments on the linear heat equation and the nonlinear Burgers'equation demonstrate that the regularized PINNs framework accurately achieves the target terminal state while producing controls with significantly lower energy and smoother profiles compared to unregularized baselines.