This is known to increase training success significantly and comes at no additional In this case that means calculating the squared error for a single prediction of hydrogen and nitrogen (indices 𝑖 = 0 and 𝑖 = 1) molar fraction in equilibrium and averaging them (𝐾 = 2 for the network construction presented in this work). Further aggregating ℓ over one minibatch (eq.( )) yields the so called MSE (mean square error) loss often used in fitting problems .