The dimensions of the input, hidden, and output layers of the GC-DNN are dim(𝝍) , the dimension of the experimental state 𝝍 , 10, and 2, respectively. For the 𝛾 DCT -DNN, the dimensions of the input, hidden, and output layers are dim(𝝍) + 1 (where the +1 comes from log 𝜏), 50, and 1, respectively. As shown in our recent works , pretraining enables us to significantly reduce the training time. Therefore, we pretrained the DNNs using the DCT obtained from ridge regression with the pyDRTtools . For both DNNs, the weights and biases were initialized with Xavier uniform method and to zero, respectively. Furthermore, the input and hidden layers of both DNNs were equipped with non-saturating exponential linear units , while the softplus activation function was used at the DNNs output layer to enforce the non-negativity constraint on the DCT . Additionally, we used the following loss function to train the DNN: The Adam algorithm was used with a learning rate of 10 -5 to minimize ℒ (𝐺 0,DNN (𝝍), 𝐶 0,DNN (𝝍), 𝜸 DNN (log 𝝉 , 𝝍)) in ( ) . Unless otherwise specified, the maximum number of iterations was set to 1.00×10 5 . When needed, DCT peaks were separated using a modification of peak-separation algorithm available in pyDRTtools (Sections 5.2.1.1 and 5.2.1.3) , and the DCT was integrated with the trapezoidal rule (Section 5.2.1.3).