  This paper presents a novel and fast solver for the J2-perturbed Lambert
problem. The solver consists of an intelligent initial guess generator combined
with a differential correction procedure. The intelligent initial guess
generator is a deep neural network that is trained to correct the initial
velocity vector coming from the solution of the unperturbed Lambert problem.
The differential correction module takes the initial guess and uses a forward
shooting procedure to further update the initial velocity and exactly meet the
terminal conditions. Eight sample forms are analyzed and compared to find the
optimum form to train the neural network on the J2-perturbed Lambert problem.
The accuracy and performance of this novel approach will be demonstrated on a
representative test case: the solution of a multi-revolution J2-perturbed
Lambert problem in the Jupiter system. We will compare the performance of the
proposed approach against a classical standard shooting method and a
homotopy-based perturbed Lambert algorithm. It will be shown that, for a
comparable level of accuracy, the proposed method is significantly faster than
the other two.
