We perform uncertainty propagation to an event manifold for Guidance \& Control Networks (G\&CNETs), aiming to enhance the certification tools for neural networks in this field.

This work utilizes three previously solved optimal control problems with varying levels of dynamics nonlinearity and event manifold complexity.

The G\&CNETs are trained to represent the optimal control policies of a time-optimal interplanetary transfer, a mass-optimal landing on an asteroid and energy-optimal drone racing, respectively.

For each of these problems, we describe analytically the terminal conditions to an event manifold with respect to initial state uncertainties. Crucially, this expansion does not depend on time but solely on the initial conditions of the system, thereby making it possible to study the robustness of the G\&CNET at any specific stage of a mission defined by the event manifold.

Once this analytical expression is found, we provide confidence bounds by applying the Cauchy-Hadamard theorem and perform uncertainty propagation using moment generating functions.

While Monte Carlo-based (MC) methods can yield the results we present, this work is driven by the recognition that MC simulations alone may be insufficient for future certification of neural networks in guidance and control applications.