Recovering the DRT, γ(log τ ), from experimental EIS data involves solving an ill-posed inverse problem, necessitating methods that ensure solution stability and prevent overfitting. While various approaches have emerged, including Fourier transform , neural networks , regularized regression , and Gaussian processes , regularized regression has gained widespread adoption. This method estimates the DRT by solving the following optimization problem :