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- W4287134920 abstract "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 introducing invertible surrogate models that approximate complex forward simulation of the physics involved in laser plasma accelerators: iLWFA. The bijective design of the surrogate model also provides all means for reconstruction of experimentally acquired diagnostics. The quality of our invertible laser wakefield acceleration network will be verified on a large set of numerical LWFA simulations." @default.
- W4287134920 created "2022-07-25" @default.
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- W4287134920 date "2021-06-01" @default.
- W4287134920 modified "2023-10-18" @default.
- W4287134920 title "Invertible Surrogate Models: Joint surrogate modelling and reconstruction of Laser-Wakefield Acceleration by invertible neural networks" @default.
- W4287134920 doi "https://doi.org/10.48550/arxiv.2106.00432" @default.
- W4287134920 hasPublicationYear "2021" @default.
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