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Found 6 papers, 2 papers with code
Learning Electron Bunch Distribution along a FEL Beamline by Normalising Flows
Understanding and control of Laser-driven Free Electron Lasers remain to be difficult problems that require highly intensive experimental and theoretical research.
Continual learning autoencoder training for a particle-in-cell simulation via streaming
These simulations will have a high spatiotemporal resolution, which will impact the training of machine learning models since storing a high amount of simulation data on disk is nearly impossible.
Invertible Surrogate Models: Joint surrogate modelling and reconstruction of Laser-Wakefield Acceleration by invertible neural networks
Invertible neural networks are a recent technique in machine learning promising neural network architectures that can be run in forward and reverse mode.
Data-Driven Shadowgraph Simulation of a 3D Object
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.
Large-scale Neural Solvers for Partial Differential Equations
However, recent numerical solvers require manual discretization of the underlying equation as well as sophisticated, tailored code for distributed computing.
Quantitatively consistent computation of coherent and incoherent radiation in particle-in-cell codes - a general form factor formalism for macro-particles
Quantitative predictions from synthetic radiation diagnostics often have to consider all accelerated particles.
Computational Physics Plasma Physics
