LPA Online Workshop on Control Systems and Machine Learning

Sitzung

Poster Session B Room 2

26.01.2022, 18:30

13. In-situ ML-based surrogate model training via continual learning and streaming

Herr Varun Makdani (Helmholtz-Zentrum Dresden-Rossendorf)

26.01.22, 18:30

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...

19. Towards deep learning-accelerated particle-in-cell simulations: application to Compton scattering events

Pablo Jaime Bilbao Santiago (Golp/IPFN, Instituto Superior Tecnico, Universidade de Lisboa, Lisboa Portugal)

26.01.22, 18:45

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...

20. Simulation-Based Inference for Beam Parameter Inversion

Peter Steinbach (Helmholtz-Zentrum Dresden-Rossendorf)

26.01.22, 19:00

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...