We develop and compare enhanced neural-network solvers for one-dimensional ground-state profiles, which are solutions of $$Q^{\prime\prime}=bQ-\gamma Q^{p},\quad Q>0,\quad p>1, \quad b>0,$$the stationary profile equation for the nonlinear Schr\"odinger, generalized Korteweg--de Vries, and nonlinear Klein--Gordon equations. Its explicit \(\operatorname{sech}\)-type solutions serve as exact reference for error computation. We improve on the standard Adam-trained physics-informed neural network (PINN) baseline by introducing higher-order optimization via Gauss-Newton / Levenberg-Marquardt and L-BFGS refinements, as well as Fourier-feature PINNs, and constrained Deep Ritz (DR) formulations. In DR approaches we introduce two new neural-network formulations: a Nehari-constrained Deep Ritz method, in which the height is learned by projecting onto the Nehari/Pokhozhaev manifold, and a Weinstein Deep Ritz method, in which the profile shape minimizes the Weinstein quotient. The Deep Ritz formulations perform best overall, in particular, the Weinstein variant gives the best error-runtime balance. The neural network based solvers remain less accurate and efficient than classical Petviashvili iteration in 1D, but their mesh-free formulation and independence from exact profiles make them promising in higher dimensions and for more complicated nonlinearities and dispersive operators.