DLA-VPS (3D GAN based model)

Introuction

The 3D GAN model which reconstruct Nyx simulation data from the simulation parameter space.

We used the data Nyx which is devloped by Lawrence Berkeley National Laboratory.
Simulaiotn Info:
- Input parameters: Omega_M: [0.17, 0.5] , Omega_B: [0.03, 0.08] and hubble: [0.55, 0.85].
- Output quantities: density, temperature, rho_e, phi_grav, x-momentum, y-momentum and z-momentum.
- Resolution: 32^3 to 8192^3. (64^3 in our case.)
We save the simulation data as TFRecord formate. Click here to download the folder Nyx_tfrecords.

Step1: Modify the Nyx_Reconstruction/config/utlis/config.py of the training. The settings that are most needed to be changed are as follows:
- variable : the quantity choosing. (density, temp, rho_e, phi_grav, xmom. ymom and zmom)
- dataSetDir: the directory of the tfRecords
- logDir : the directory to save the trained models (generator and discriminator)
- epochs : the number of the training epochs
- batchSize : the batch size during training
- save_weights_only: save wieghts only or save model
- save_epochs: how many epochs to save the trained model once
Step2: Run main.py to start the training. The trained models will be saved under the logDir/gen and logDir/dis with respective to the generator and discriminator during training. We highly recommend you to traine the models on GPU instead of CPU.
Step3: Buy a coffee and have patience!

The details of the model architecture and training process can be found in the theses DLA-VPS.
Anyone is welcome to let us know how to improve in any way.