It is worth also noting the key difference between our approach and PINN models. In PINN, the knowledge about physical laws is embedded at training of ANN by using corresponding loss functions. In our case, the architecture of ANN is already intrinsically constrained by kinetic and thermodynamic knowledge. The hidden layer of the neural network with a hyperbolic tangent activation function consists of 8 nodes. To investigate the performance of KCNODE models, an analogue model with 20 nodes in the hidden layer was also constructed.