| Summary: | The variation of shock-wave velocity with time induced by a millisecond-nanosecond combined pulse laser (CPL) on silicon is investigated. The convolution neural network (CNN) is used to predict the shock-wave velocity induced by a single ns laser and CPL with a ns laser energy density of 6, 12 and 24 J/cm<sup>2</sup>, ms laser energy density of 0 and 226.13 J/cm<sup>2</sup>, and pulse delay of 0, 0.4 and 0.8 ms. The four-layer CNN model was applied, ns laser energy density, ms laser energy density, pulse delay and time were set as the input parameter, while the shock-wave velocity was set as the output parameter. The correlation coefficient (<i>R</i><sup>2</sup>), mean absolute error (<i>MAE</i>) and root mean square error (<i>RMSE</i>) of the CNN model on the test data set was 0.9865, 3.54 and 3.01, respectively. This indicated that the CNN model shows a high reliability in the prediction of CPL-induced shock-wave velocity with limited experimental data.
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