1 citations · 2 across the 4 of their papers we have counts for
5 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…
Physics-Informed Neural Networks as Solvers for the Time-Dependent Schrödinger Equation
Karan Shah, Patrick Stiller, Nico Hoffmann +1
We demonstrate the utility of physics-informed neural networks (PINNs) as solvers for the non-relativistic, time-dependent Schrödinger equation. We study the performance and genera…
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…