学术报告
报告题目： Data-driven rogue waves and parameter discovery in the defocusing NLS equation with a potential using the PINN deep learning
报告人：闫振亚，中国科学院数学与系统科学研究院研究员，博士生导师
报告时间：2021年4月18日
腾讯会议：会议 ID：109 700 288
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报告摘要：
The physics-informed neural networks (PINNs) can be used to deep learn the nonlinear partial differential equations and other types of physical models. In this talk, we use the multi-layer PINN deep learning method to study the data-driven rogue wave solutions of the defocusing nonlinear Schrödinger (NLS) equation with the time-dependent potential by considering several initial conditions such as the rogue wave, Jacobi elliptic cosine function, two-Gaussian function, or three-hyperbolic-secant function, and periodic boundary conditions. Moreover, the multi-layer PINN algorithm can also be used to learn the parameter in the defocusing NLS equation with the time dependent potential under the sense of the rogue wave solution. These results will be useful to further discuss the rogue wave solutions of the defocusing NLS equation with a potential in the study of deep learning neural networks.
专家简介：
闫振亚博士，中国科学院数学与系统科学研究院研究员，主要研究数学物理、非线性波和可积系统的理论方法，符号与数值计算及应用，包括怪波理论、反散射理论、Riemann-Hilbert问题与方法、非厄米PT-对称分析及应用等等。曾获全国百篇优秀博士论文奖、首届中科院卢嘉锡青年人才、中科院优秀导师奖等。