Abstract Physics informed neural network (PINN) demonstrates powerful capabilities in solving forward and inverse problems of nonlinear partial differential equations (NLPDEs) through combining data-driven and physical constraints. In this paper. two PINN methods that adopt tanh and sine as activation functions. respectively. https://foldlyers.shop/product-category/terminal-blocks/