###### Abstract
These models necessitate specific architectural designs and training for particular conditions, treating the fiber system as a black box and learning the time and frequency domain signals of input-output relationships. Once the fiber configuration changes, the learned input-output relationships also change. Consequently, while these neural networks can handle different lengths and input signals, they cannot adapt to fiber systems with varying parameters [15; 16; 17]. In this study, we propose an advanced DeepONet model architecture integrated with an attention mechanism for full-field pulse transmission simulation. Research on RNNs, CNNs, and LSTMs suggests their potential; thus, they are incorporated into the Branch network of DeepONet to process the input function values at fixed sensing points. This enables the learning of relationships between input and output signals through multilayer neural networks. The Trunk network handles the locations encoding the output function, which includes the initial pulse parameters such as Full Width at Half Maximum (FWHM) of the spectrum, input peak power, fiber length and propagation distance, and linear and nonlinear model parameters like dispersion and nonlinear coefficients. The integration of an attention mechanism [18] aims to enhance the model's competency in managing complex input-output relationships. By dynamically assigning importance weights to various input features, the attention mechanism effectively captures crucial information within the input signals, thereby improving the model's learning efficiency and prediction accuracy. In the Branch network, the attention mechanism highlights essential features within the input signals, enhancing the comprehension of multi-level input information. In the Trunk network, it ensures the precise handling of the relationship between output location encoding and the corresponding initial pulse and transmission parameters, bolstering the overall simulation accuracy. The integrated attention mechanism within the DeepONet model not only excels in solving complex nonlinear fiber transmission problems but also enhances the overall accuracy and robustness of full-field pulse transmission simulation by emphasizing critical input features. By training the DeepONet model architecture on the NLSE equation, we observed several advantages:  
1. **Extremely fast generation speed**: Since the simulation process eliminates the need for iterative calculations, only a single computation is required regardless of the parameters and initial conditions. The pulse generation speed, recorded at approximately 0.0014 s (GPU: RTX 4090, PyTorch V2.3.1, CUDA V12.1), is significantly faster than the step-by-step calculations of SSFM and previous iterative neural network models for long-distance transmission. 2. **Enhanced generalization ability and high accuracy**: Compared to RNN, CNN, and LSTM models [15; 16; 17], DeepONet demonstrates superior generalization ability without a substantial loss in accuracy. The training results showcase its high stability and strong generalization capability. 3. **Flexible input pulse processing**: The Branch network can handle dynamic input pulse lengths without the need to truncate pulses to reduce complexity. Pulse length only affects the uniformity of input pulse accuracy without losing critical data.