Data-driven solutions and parameter estimations of a family of higher-order KdV equations based on physics informed neural networks
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 nacrack.com as activation functions, respectively, are used to