Using CycleGAN with physical inconsistency loss for a numerical problem

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Hi Rushil,
Greetings. I am trying to use your CycleGAN architecture with physical inconsistency loss in my master thesis at Airbus. For this project, we have to generate a large nr. of synthetic samples (basically transform the output from a CFD Solver) that match the distribution of some real-world lab test points (which are very sparse and limited). The data points are numerical values and there are no images to generate here for the CycleGAN. This idea is explained in the image below where the dotted green line was drawn by hand.

We made a small toy problem where I use normal CycleGAN and documented some notes and observations. You can find the toy problem and the notes in the links below, in case you want to know additional details.
i.) [CycleGAN - Toy Problem](https://colab.research.google.com/drive/1IgipvRe7rFzijN316t2fD3sdMScjFwMM)
ii.) [CycleGAN - Notes](https://drive.google.com/file/d/1eZF3JWGowDOm8HzNkc6CJBeATTxFjoA1/view?usp=sharing)

I have a few clarifications for which I would like to request your kind feedback:
1.) Do you think your _CycleGAN architecture with physical inconsistency loss_ will help in my case to achieve the intended goal (as in the above image) of transforming the simulation points to look like real-world lab test points?
2.) Would it be possible for you to share the [tensorflow implementation of your CycleGAN](https://github.com/rushilanirudh/macc/blob/master/modelsv2.py) in Keras or at least give me pointers for translating your tensor flow code for the discriminator, generator and your `def_run()` function with physical inconsistency loss into the Keras style code as in my Toy Problem?

Best Regards,
Dilip

Start by reading modelsv2.py and comparing its discriminator, generator, and def_run() implementation with the linked Colab toy problem and notes. Before making changes, clarify whether the expected outcome is feasibility guidance, a Keras translation, or both; completion criteria are not defined in the issue.

Written by the indexing model from the issue text.