Sprecher
Beschreibung
Laser-Plasma Acceleration (LPA) is a highly non-linear process sensitively dependent on parameters of gas flow and laser which are hard to control or simultaneously measure in experiments. Understanding of such parameter dependencies can be driven by simulations which offer control and observability, but are more expensive the more physical details are included. In the case of LPA, full 3D particle-in-cell (PIC) simulations are far to expensive to allow detailed scans of the available parameter space. Deep-learning based surrogate models are promising for guiding parameter optimizations, enable fast result estimation or inversion and compile information about vast parameter spaces more effectively. In this talk we are going to review state-of-the-art approaches to surrogate modeling in plasma physics to show perspectives and challenges in leveraging approaches like neural operator and physically-informed neural networks (PINNs) to enhance simulation-driven progress in LPA.
