Issue with validation_exp(), validation_mf

[lim919]

Hello professor, thanks for your kindness and your work. I'm a student following your code work, and I faced some issues while operating mfnn code. I want to ask you some questions.

1. When I operate validation_exp(), I obtain two MAPE and STD with two vector form. "sigma_y [52.81542692 20.47970335] [11.47603269 2.44488563]" I'm expecting the 52.815 as MAPE of lowfidelity NN output, and 20.479 as MAPE of total NN output. Can you tell me either my expectation is right? if it's not, can you tell me what that features mean?

2. I have operated 2 cases of validation_exp() - (1) Al6061, (2) B3067, and I obtained significantly different MAPE with Sy against your paper work.

For case(1) Al6061, Paper(Fig. 4. A) suggested about 15% MAPE with NN(2D+3D). But when I try to get MAPE for NN(2D+3D), I get significantly large MAPE of it. Below is the result of my code operation. "sigma_y [3683.31674364 102.5687206 ] [3102.81756024 72.03417236]"

For case(2) B3067, Paper(Fig. 6. B) suggested 105.5% MAPE with NN(raw). But when I try to get MAPE for NN(raw), I get different MAPE value as before in case(1). Below is the result of my code operation. "sigma_y [52.81542692 20.47970335] [11.47603269 2.44488563]"

I'm confused with the two different attitude of same code: in case(1)-Al6061, it gives me larger MAPE but in case(2)-B3067, it gives me smaller MAPE. I want to know why I'm getting a wrong MAPE value with validation_exp() operation. Was there any training dataset adjustments when you validate those values in paper?

3. This question is related to Q1. On the bottom of validation_mf() code, mape.append(dde.utils.apply(mfnn, (data,))[0])

this is different with that of validation_exp(), res = dde.utils.apply(mfnn, (data,)) ape.append(res[:2])

First code gives only one MAPE value, but second code gives two MAPE values and it seems first value among two values in second code is equal to the value that is given by first code. In Fig. 3. D in your paper with High-fidelity Training Data Set Size of 12, MAPE is suggested about 22%, and if I operate code with two different codes, I obtain 22.575627271690337 11.91528043968248 and [22.18273957 9.71804237] [12.79467543 12.11059052]. If my expectation is right as Q1, the value of MAPE in this case should be 9.718 but paper suggested 22 for the result. If my expectation is right, can you tell me why did you used 22% of MAPE as result of Fig. 3. D?

These are the questions and issues I met with operating your code work. Thanks again with your hard work and giving access to your code work.

Sincerely, Koo.