Next, we tested how well the DNN-DRT model could recover discontinuous DRTs by studying the fractal, PWC, and Gerischer models (the parameters are given in Tables S21, S22, and S23, respectively). For all three models and 𝑁 = 1, 10, and 100 𝑀, the peak was approximately identified during RR, as shown in Figures , and S10. This was confirmed by the large values of 𝜀 , (𝜀 , = 77.15%, 29.51%, and 67.95% for the fractal, PWC, and Gerischer models, respectively, see Table for all 𝑁). Training the DNN enabled us to increase the accuracy, as evidenced by the lower values 𝜀 , compared to 𝜀 , ( 𝜀 , ≤ 75.89%, 16.99%, and 66.07%, see Table ). Furthermore, when 𝑁 increased from 𝑀 to 10 𝑀, 𝜀 , decreased for the fractal and Gerischer models but increased for the PWC model; then, 𝜀 , increased for all three models when 𝑁 varied from 10 𝑀 to 100 𝑀 (Table ). Similarly, the impedance recovery was slightly improved by DNN training compared to RR, see Figures , and S10, and the values of the impedance errors (𝜀̅ , = 6.94%, 5.37%, and 6.00%, versus 𝜀̅ , ≤6.91%, 4.17%, and 4.16%, see Table ). Interestingly, 𝜀̅ , decreased for the fractal model and did not vary for the PWC and Gerischer models, see Table . We validated these results by drawing ten synthetic EIS spectra (Tables and, and the boxplots in Figure ).