In convection-dominated regimes, solutions of convection-diffusion problems often have steep gradients. Traditional numerical methods struggle with these issues. Recently, physics-informed neural networks (PINNs) have emerged, minimizing residuals at collocation points. This talk introduces specialized loss functionals for PINNs tailored to these problems.