Data-driven parity-time-symmetric vector rogue wave solutions of multi-component nonlinear Schr?dinger equation

【摘要】 Rogue waves are a class of nonlinear waves with extreme amplitudes, which usually appear suddenly and disappear without any trace. Recently, the parity-time(PT)-symmetric vector rogue waves(RWs) of multi-component nonlinear Schro¨dinger equation(n-NLSE) are usually derived by the methods of integrable systems. In this paper, we utilize the multi-stage physics-informed neural networks(MS-PINNs) algorithm to derive the data-driven symmetric vector RWs solution of coupled NLS system in elliptic and X-shapes domains with nonzero boundary condition. The results of the experiment show that the multi-stage physics-informed neural networks are quite feasible and effective for multi-component nonlinear physical systems in the above domains and boundary conditions.更多还原

【Abstract】 Rogue waves are a class of nonlinear waves with extreme amplitudes, which usually appear suddenly and disappear without any trace. Recently, the parity-time(PT)-symmetric vector rogue waves(RWs) of multi-component nonlinear Schro¨dinger equation(n-NLSE) are usually derived by the methods of integrable systems. In this paper, we utilize the multi-stage physics-informed neural networks(MS-PINNs) algorithm to derive the data-driven symmetric vector RWs solution of coupled NLS system in elliptic and X-shapes domains with nonzero boundary condition. The results of the experiment show that the multi-stage physics-informed neural networks are quite feasible and effective for multi-component nonlinear physical systems in the above domains and boundary conditions.更多还原