Invertible Surrogate Models: Joint surrogate modelling and reconstruction of Laser-Wakefield Acceleration by invertible neural networks
arXiv:2106.00432
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.
References in corpus (8)
- DGM: A deep learning algorithm for solving partial differential equations
- Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equations
- Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations
- Density estimation using Real NVP
- Learning to Simulate Complex Physics with Graph Networks
- Glow: Generative Flow with Invertible 1x1 Convolutions
- Analyzing Inverse Problems with Invertible Neural Networks
- Solving inverse problems using conditional invertible neural networks