A Structure-Preserving Neural-Spectral Method for Reconstructing Controls of Wave Equations
The numerical reconstruction of controls for partial differential equations remains comparatively underdeveloped, despite the extensive analytical literature on controllability. This difficulty is particularly pronounced for wave equations, whose conservative structure, oscillatory dynamics, and high-frequency behavior make direct discretization and optimization challenging. In this work, we introduce a Neural-Spectral method for approximating controls of wave equations. The method represents both the state and the control in a Dirichlet spectral basis and parameterizes the time-dependent modal coefficients using shallow neural networks. In this way, the spatial oscillatory structure of the wave equation is built into the approximation, and the learning task is reduced to reconstructing temporal coefficients. We prove approximation results showing that, under the standing assumption that an exact control exists in the relevant energy framework, the control-state pairs found can approximate exact controlled trajectories uniformly in time in the energy norm, while also approximating the corresponding controls in \(L^2\). We also state a conditional computable error estimate that separates spectral truncation, neural-network approximation, quadrature, and optimization errors. In addition, we discuss structural obstructions faced by standard time-stepping schemes for conservative wave dynamics: explicit Euler amplifies high frequencies, implicit Euler introduces artificial dissipation, and Crank--Nicolson preserves amplitudes but compresses high-frequency phases. Numerical experiments in one, two, and three space dimensions illustrate the method on nonlinear, linear-reference, and high-dimensional control benchmarks.