The field of physics-informed neural networks (PINNs) is rapidly advancing, with a focus on improving the accuracy and efficiency of solving partial differential equations (PDEs). Recent developments have introduced innovative methods, such as gradient-enhanced self-training PINNs, which have shown promising results in solving nonlinear PDEs. These methods have demonstrated better generalization and convergence capabilities compared to traditional PINNs. Noteworthy papers in this area include the Gradient Enhanced Self-Training Physics-Informed Neural Network (gST-PINN) for Solving Nonlinear Partial Differential Equations, which proposed a novel method that outperforms standard PINNs in various scenarios. The Neural Network approximation power on homogeneous and heterogeneous reaction-diffusion equations paper provided a theoretical analysis of the approximation power of neural networks for reaction-diffusion equations, highlighting their expressive power in approximating solutions to PDEs.