利用物理信息神经网络预测基于复PT对称势的孤子动力学

We examine the deep learning technique referred to as the physics-informed neural network method for approximating nonlinear Schr?dinger equation under considered parity time symmetric potentials and obtaining multifarious soliton solutions. For the first time, neural networks founded principally physical information are adopted to figure out the solution the examined nonlinear partial differential equation and generate six different types of soliton solutions, which are basic, dipole, tripole, quadruple, pentapole and sextupole solitons we consider. We make comparisons between the predicted and actual soliton solutions to see whether deep learning is capable of seeking the solution the partial differential equation described before. We may assess whether physics-informed neural network is capable of effectively providing approximate soliton solutions through the evaluation of squared error between the predicted and numerical results. Besides, we also scrutinize how different activation mechanisms and network architectures impact the capability of selected deep learning technique works.Through the findings we can prove that the neural networks model we established can be utilized to accurately and effectively approximate nonlinear Schr?dinger equation under consideration and predict the dynamics of soliton solution.