**Appendix A**

(**a**) MLP Fraction (**b**) MLP Temperature **Figure A1.** Predicted void fraction and wall temperature using DNN / MLP model for an interpolation dataset at *q* = 14,000 W/m2, u = 0.05 ms<sup>−</sup>1, from the plot it can be noted it has high nonlinearity while predicting the wall temperature.

**Figure A2.** Comparison between CFD and predicted void fraction and wall temperature along the arc length for an interpolation dataset at *q* = 14,000 W/m2, u = 0.05 ms<sup>−</sup>1. From the plot it can be noted that the MLP model has some artefacts while predicting the void fraction. It showed a sharp jump in void fraction around 0.8 arc length where the nucleation starts in the minichannel.

**Figure A3.** Regression chart of CFD vs DNN/MLP predicted void fraction and wall temperature for an extrapolation dataset at *q* = 40,000 W/m2, u = 0.2 ms<sup>−</sup>1. From both the plot it is evident that the MLP model lacks in reproducing the physics on an extrapolated dataset.

 vs

**Figure A4.** Comparison between CFD and DNN/MLP model predicted void fraction and wall temperature along the arc for an interpolation dataset at *q* = 40,000 W/m2, u = 0.2 ms<sup>−</sup>1. From the plot it can be noted that the MLP model shows overconfident values for the void fraction and under predicted values for the wall temperature. Although the MLP model fails to capture the physics accurately it still showed good trend to that of CFD data.

(**b**) CFD vs DNN predicted Temperature

**Figure A5.** Interpolation dataset: Void fraction and temperature field of CFD and predicted by the DNN/MLP model and the relative error for an interpolation dataset at *q* = 14,000 W/m2, u = 0.05 ms<sup>−</sup><sup>1</sup> is presented. The plot above presents the predictive nature of the DNN model to predict the full flow field of the mininchannel. It can be seen that the DNN model is capable of reproducing the void fraction field with a miximum relative error of −6%. It can be further noted that the error increases as the void fraction increases in the mininchannel. Temperature field using the DNN model is presented in Figure A5b. Compared to the void fraction prediction the DNN model has better performance when predicting the temperature field with a maximum relative error of 0.3%.

**Figure A6.** Interpolation dataset: Comparison of CFD and DE model prediction for *q* = 14,000 W/m2, u = 0.05 ms<sup>−</sup>1. It can be noted from Figure A6a that the DE model shows good performance when predicting the void fraction field with a maximum relative error of 0.77%. Similarly, the DE model shows an exceptional predicting capability for the temperature field with a maximum relative error of 0.13%. From this, it can be concluded the DE model shows almost an order of better accuracy when compared to the DNN model for the interpolation datasets.

(**b**) CFD vs DNN predicted Temperature

**Figure A7.** Extrapolation dataset: The CFD, DNN prediction and the relative error for *q* = 40,000 W/m2, u = 0.2 ms<sup>−</sup>1. It can be depicted from Figure A7a that the DNN models fail to accurately predict the void fraction field and have a maximum relative error of 10.5%. However, the DNN model shows an acceptable performance when predicting the temperature field with a maximum relative error of 0.55% as shown in Figure A7b.

**Figure A8.** Extrapolation dataset: Comparison of CFD and DE model prediction for *q* = 40,000 W/m2, u = 0.2 ms<sup>−</sup>1. It can be again noted that the DE model outperforms the DNN model when predicting both void fraction and temperature field. The DE model has maximum relative error of 1.78% for void fraction and 0.28% for temperature field as shown in Figure A8a,b.

**Figure A9.** Interpolation dataset: *q* = 14,000 W/m2, u =0.05 ms<sup>−</sup>1. In the Figure the standard deviation of the wall temperature and void fraction along the arc length for both the models. When comparing the *σ* variation between MC Dropout and DE models, it is clear that the *σ* variation of DE is smaller by approximately one order of magnitude. The correlation and sensitivity that exist between the wall temperature and void fraction is shown. From the plot is evident that slight change in *σ* for the void fraction influences the *σ* of wall temperature. There is a sharp increase in *σ* for the void fraction in MC dropout model and this is due to transition of regime from saturated boiling to film boiling.

**Figure A10.** Extrapolation dataset: *q* = 14,000 W/m2, u = 0.05 ms<sup>−</sup>1. It is again seen that the *σ* of the DE model is one order of magnitude lesser compared to that of the MC Dropout model. This implies that the DE model is less uncertain about its predicted value and is more robust in nature.
