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We adopted a three-layer feedforward artificial neural network and used Pytorch to simulate the relationship between age and telomere length. This network consists of two hidden layers with ReLU activation functions and an output layer with a linear activation function. During the forward propagation process, the model takes an individual's age as input (x) and processes it through the network, applying weighted sums and activation functions at each layer. The final output represents the model's prediction of telomere length (y). This network is trained using supervised learning methods, and the weights and biases of each layer are adjusted through gradient descent to minimize the mean square error between the predicted telomere length and the actual telomere length. This process is essentially about learning the complex nonlinear mapping between age and telomere length, which is represented by the structure and parameters of the network.