  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.
