基于深度学习的Manakov系统矢量局域波及参数发现
Data-driven vector localized waves and parameters discovery for Manakov system using deep learning approach
摘要：
提出了一种改进的物理信息神经网络（IPINN）算法，该算法具有4个输出函数和4个物理约束，并具有神经元局部自适应激活函数和斜率恢复项，用于求解Manakov系统在初始和边界条件下的数据驱动矢量局域波，包括矢量孤子、呼吸子和流氓波，以及参数未知的Manakov系统的数据驱动参数发现。为了验证IPINN算法训练复杂局域波的能力，学习了数据驱动的矢量RWs，其中包含RWs与亮暗孤子的相互作用波、RWs与呼吸子的相互作用波以及由亮暗孤子演化而来的RWs。在参数发现过程中，常规IPINN无论使用干净数据还是噪声数据都不能准确训练未知参数。因此，我们将权系数可调的参数正则化策略引入到IPINN中，有效准确地训练预测参数，发现在使用噪声数据时，只要设置适当的权系数，训练效果更好。数值结果表明，参数正则化的IPINN在参数发现问题中具有较好的抗噪性。
英文摘要：
An improved physics-informed neural network (IPINN) algorithm with four output functions and four physics constraints, which possesses neuron-wise locally adaptive activation function and slope recovery term, is appropriately proposed to obtain the data-driven vector localized waves, including vector solitons, breathers and rogue waves (RWs) for the Manakov system with initial and boundary conditions, as well as data-driven parameters discovery for Manakov system with unknown parameters. The data-driven vector RWs which also contain interaction waves of RWs and bright-dark solitons, interaction waves of RWs and breathers, as well as RWs evolved from bright-dark solitons are learned to verify the capability of the IPINN algorithm in training complex localized wave. In the process of parameter discovery, routine IPINN can not accurately train unknown parameters whether using clean data or noisy data. Thus we introduce parameter regularization strategy with adjustable weight coefficients into IPINN to effectively and accurately train prediction parameters, then find that once setting the appropriate weight coefficients, the training effect is better as using noisy data. Numerical results show that IPINN with parameter regularization shows superior noise immunity in parameters discovery problem.
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