Numerical simulations of complex systems such as Laser-Plasma acceleration are computationally very expensive and have to be run on large-scale HPC systems. Offline analysis of experimental data is typically carried out by expensive grid scans or optimisation of particle-in-cell code like PIConGPU modelling the corresponding physical processes. Neural Network based surrogate models of this...
We present a novel method to efficiently implement Machine Learning methods within Particle-in-Cell (PIC) simulation codes. Such codes are vital to fully understand the kinetic processes involved in Laser Wakefield acceleration and constitute a key tool to comprehend experimental setups and their diagnostics data. However, their computational cost prevents large parameter scans in 3D...
In this talk, I'd like to present modern machine learning tools for estimating the posterior of the inverse problem exposed in a beam control setting. That is, given an experimental beam profile, I'd like to demonstrate tools that help to estimate which simulation parameters might have produced a similar beam profile with high likelihood.
We summarize preliminary findings bound to optimize...
