Discretization of the DRT was achieved using piecewise linear functions , and the timescales were chosen to be the inverse of the frequencies. The 𝒙 ̂𝑝,𝑞 of each synthetic experiment was obtained using CVXPY with the GUROBI optimizer . To find the best regularization parameter 𝜆 , we employed Bayesian optimization and minimized 𝜀 𝛾 (Figure (a)). Unless otherwise specified, a third-order differentiation matrix, 𝑳 3 , was used to solve (2). To assess robustness against outliers (Section 3.1.3), we used the distant 2×ZARC model and arbitrarily set the experimental noise at the frequencies 0.01, 1.0, and 10 4 Hz to be 25 times higher than the baseline white noise (Figure (a)). Lastly, the impedances were estimated as 𝑨𝒙 ̂𝑝,𝑞 .