Herein, we used two feedforward deep neural networks (DNNs). The first network, denoted as GC-DNN, takes a vector identifying experimental conditions, such as temperature and pressure, and generically indicated as 𝝍, to return the model parameters 𝐶 0 and 𝐺 ∞ (given (3)). A second DNN, called 𝛾 DCT -DNN, takes the vector of experimental conditions and the specific log timescale considered, log 𝜏, to output the DCT. 𝛾 DCT -DNN can be ultimately understood as a generic function with inputs 𝝍 and log 𝝉 . Both DNNs are simple feedforward dense neural networks with five layers, including one input layer, three hidden layers, and one output layer.