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Found 4 papers, 1 papers with code
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.
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