High-order harmonic generation is one of the ways to generate attosecond ultra-short pulses. In order to accurately simulate the high-order harmonic emission, it is necessary to perform fast and accurate calculations on the interaction between the atoms and strong laser fields. The accurate profile of the laser field is obtained from the propagation through the gas target. Under the conditions of longer wavelength driving lasers and higher gas densities, the calculation of the laser field becomes more challenging. In this paper, we utilize the driving laser electric field information obtained from numerically solving the three-dimensional Maxwell’s equations as data for machine learning, enabling the prediction of the propagation process of intense laser fields using an artificial neural network. It is found that the simulation based on frequency domain can improve the accuracy of electric field by two orders of magnitude compared with the simulation directly from time domain. On this basis, the feasibility of the transfer learning scheme for laser field prediction is further studied. This study lays a foundation for the rapid and accurate simulation of the interaction between intense laser and matter by using an artificial neural network scheme.
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