18 citations · 19 across the 4 of their papers we have counts for
8 papers
Continual learning autoencoder training for a particle-in-cell simulation via streaming
Patrick Stiller, Varun Makdani, Franz Pöschel +4
The upcoming exascale era will provide a new generation of physics simulations. These simulations will have a high spatiotemporal resolution, which will impact the training of mach…
Invertible Surrogate Models: Joint surrogate modelling and reconstruction of Laser-Wakefield Acceleration by invertible neural networks
Friedrich Bethke, Richard Pausch, Patrick Stiller +3
Invertible neural networks are a recent technique in machine learning promising neural network architectures that can be run in forward and reverse mode. In this paper, we will be…
Data-Driven Shadowgraph Simulation of a 3D Object
Anna Willmann, Patrick Stiller, Alexander Debus +5
In this work we propose a deep neural network based surrogate model for a plasma shadowgraph - a technique for visualization of perturbations in a transparent medium. We are substi…
Large-scale Neural Solvers for Partial Differential Equations
Patrick Stiller, Friedrich Bethke, Maximilian Böhme +6
Solving partial differential equations (PDE) is an indispensable part of many branches of science as many processes can be modelled in terms of PDEs. However, recent numerical solv…
Laser-plasma proton acceleration with a combined gas-foil target
Dan Levy, Constantin Bernert, Martin Rehwald +10
Laser-plasma proton acceleration was investigated in the Target Normal Sheath Acceleration (TNSA) regime using a novel gas-foil target. The target is designed for reaching higher l…
Probing Ultrafast Magnetic-Field Generation by Current Filamentation Instability in Femtosecond Relativistic Laser-Matter Interactions
G. Raj, O. Kononenko, A. Doche +28
We present experimental measurements of the femtosecond time-scale generation of strong magnetic-field fluctuations during the interaction of ultrashort, moderately relativistic la…