  We propose an abstract framework for analyzing the convergence of
least-squares methods based on residual minimization when feasible solutions
are neural networks. With the norm relations and compactness arguments, we
derive error estimates for both continuous and discrete formulations of
residual minimization in strong and weak forms. The formulations cover recently
developed physics-informed neural networks based on strong and variational
formulations.
