The JAG inertial confinement fusion simulation dataset for multi-modal scientific deep learning. In Lawrence Livermore National Laboratory (LLNL) Open Data Initiative

A dataset is provided to test/train the models. This is a tarball inside 'data/', which contains .npy files for 10K images, scalars, and the corresponding input parameters. The size of the dataset provided (in 'data/') are as follows: Input: (9984, 5), Output/Scalars: (9984, 22), Output/Images: (9984, 16384). Images are interpreted as (-1,64,64,4). This package was built and tested using Tensorflow 1.8.0. It also depends on standard Python packages such as NumPy, Matplotlib for basic data loading and plotting utilities. We also provide a Python Jupyter Notebook, that is a self-contained script to load, process, and test the dataset described above. In particular, we include a Neural Network designed to act as a surrogate for the JAG 1D Simulator. The neural network is implemented in Tensorflow. The notebook allows a user to load the dataset, load the neural network and train it such that given just the 5 input parameters, it predicts the scalars and images accurately. This can be done directly in the notebook, without any additional modifications. During training, intermediate predictions are also saved to disk (as specified by the user). We hope this serves as a starting point to build, test and play with the ICF-JAG simulation dataset.