Fig. 1. Schematic of the training process.

Fig. 2. Training and validation loss as well as accuracy of the neural network which contains three middle hidden layers with 128, 64 and 32 neurons, respectively.

Fig. 3. Applying the ANN to the theoretical models. Seven arbitrary particle distributions predicted by the ANN (right-hand panel) and their corresponding fitting curves (left-hand panel) compared with the initial theoretical models.

Fig. 4. The nanoparticle distributions generated from shock-compressed PET obtained by the ANN, Monte Carlo method and analytical model (left-hand panel) and their corresponding SAXS fitting curves compared with the experimental data (right-hand panel). The red dots indicate the resulting mean particle radius from the three methods. The color bar represents the various probing times.