diff --git a/Docs/source/usage/workflows/ml_dataset_training.rst b/Docs/source/usage/workflows/ml_dataset_training.rst index 115420f1923..450e0fe2879 100644 --- a/Docs/source/usage/workflows/ml_dataset_training.rst +++ b/Docs/source/usage/workflows/ml_dataset_training.rst @@ -14,10 +14,11 @@ For example, a simulation determined by the following input script .. literalinclude:: ml_materials/run_warpx_training.py :language: python -In this section we walk through a workflow for data processing and model training. +In this section we walk through a workflow for data processing and model training, using data from this input script as an example. +The simulation output is stored in an online `Zenodo archive `__, in the ``lab_particle_diags`` directory. +In the example scripts provided here, the data is downloaded from the Zenodo archive, properly formatted, and used to train a neural network. This workflow was developed and first presented in :cite:t:`ml-SandbergIPAC23,ml-SandbergPASC24`. - -This assumes you have an up-to-date environment with PyTorch and openPMD. +It assumes you have an up-to-date environment with PyTorch and openPMD. Data Cleaning ------------- @@ -277,7 +278,7 @@ Surrogate Usage in Accelerator Physics ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ A neural network such as the one we trained here can be incorporated in other BLAST codes. -Consider `this `__ example using neural networks in ImpactX. +Consider this `example using neural network surrogates of WarpX simulations in ImpactX `__. .. bibliography:: :keyprefix: ml-