Reference dataset of multi-objective and multi-fidelity optimization in laser-plasma acceleration

This repository contains a dataset used for the article "Multi-objective and multi-fidelity Bayesian optimization of laser-plasma acceleration" (arXiv:2210.03484). The dataset consists of 2443 FBPIC particle-in-cell simulations of a laser wakefield accelerator that were selected using a Bayesian optimizer. The goal of the optimization was to perform multi-objective multi-fidelity optimization of electron beam parameters. The dataset contains simulations of different resolutions, accordingly with differing fidelities. The typical runtime at lowest (highest) resolution is approximately 1 (90) minutes.

In the dataset we have train_x and train_obj numpy arrays with dimensions (n,5) and (n,3), respectively. Here n is the number of FBPIC simulations. The five columns in train_x are [plasma density, upramp length, laser focus, downramp length, fidelity]. The fidelity parameter controls the resolution and hence the runtime of the simulation. The three columns in the train_obj are the [total charge, distance of median to target energy, bandwidth of electron beams]. For the distance, the target energy is fixed to 300 MeV and for the bandwidth is defined by the median absolute deviation around the median. The two columns have negative values since the optimizer assumes a maximization of all objectives while the distance and bandwidth in this study were being minimized.

The different folders contain data of different kind of single and multi-objectives that were used to produce figures 2, 3, 5 in the associated paper. For more details please see the referred article. The folder "combined" contains the data of all simulations together and is most suitable for (5D x 3D) surrogate model generation.