论文标题
部分可观测时空混沌系统的无模型预测
Physics-Informed Neural Operator for Fast and Scalable Optical Fiber Channel Modelling in Multi-Span Transmission
论文作者
论文摘要
我们通过无参考溶液提出了通过NLSE受限的物理信息神经操作员对光纤通道的有效建模。对于距离，序列长度，启动功率和信号格式，该方法很容易扩展，并用于用于使用ASE噪声的16-QAM信号传输的超快速模拟。
We propose efficient modelling of optical fiber channel via NLSE-constrained physics-informed neural operator without reference solutions. This method can be easily scalable for distance, sequence length, launch power, and signal formats, and is implemented for ultra-fast simulations of 16-QAM signal transmission with ASE noise.
