It was recently shown that deep neural networks (DNNs) can be utilized to deconvolve the DRT . In particular, the first DNN-based method ever developed, called deep-prior DRT, used a DNN with a single random scalar as input and output a vector of DRT values at discrete timescales together with the circuit parameters 𝐿 and 𝑅 . The non-negativity constraint (i.e., 𝛾(log 𝜏) ≥ 0 ) illustrated above was imposed by choosing non-negative activation functions for the DNN. Unlike in conventional RR, regularization was not necessary to obtain 𝛾(log 𝜏) . Another particularly interesting aspect of the deep-prior DRT approach is that the corresponding inverse problem is overparametrized: the size of the vector of DNN parameters, including weights and biases, is significantly larger (~10 parameters) than the number of probed frequencies (~10 frequencies). As illustrated by the "doubledescent" bias-variance curve introduced by Belkin et al. , overparameterization allows for small test errors, thereby improving the DNN performance. Quattrocchi et al. further extended the deep-prior DRT approach with the deep-DRT model by considering a DNN that takes as inputs the scalar log-timescale log 𝜏 and a state vector 𝝍 = (𝜓 , … , 𝜓 ) describing experimental conditions (e.g., temperature, pressure, etc.) . In addition to inverting the DRT and regressing the experimental impedance, the trained deep-DRT model was used to predict the DRT (and corresponding impedance) at experimental states not tested experimentally.