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Invertible neural networks are a recent technique in machine learningpromising neural network architectures that can be run in forward and reversemode. In this paper, we will be introducing invertible surrogate models thatapproximate complex forward simulation of the physics involved in laser plasmaaccelerators: iLWFA. The bijective design of the surrogate model also providesall means for reconstruction of experimentally acquired diagnostics. Thequality of our invertible laser wakefield acceleration network will be verifiedon a large set of numerical LWFA simulations.

Invertible Surrogate Models: Joint surrogate modelling and reconstruction of Laser-Wakefield Acceleration by invertible neural networks