To establish a theoretical framework for mapping Gaussian statistical distributions through nonlinear dynamics and apply it to spacecraft trajectory optimization and uncertainty analysis.
Formulated mathematical transformations for the propagation and mapping of Gaussian probability density functions across nonlinear dynamical regimes.
Applied the analytical mapping framework to astrodynamics models and spacecraft trajectory design problems under orbital uncertainty.
Developed theoretical methods enabling accurate tracking and transformation of nonlinearly mapped Gaussian statistical states.
Demonstrated practical applicability for astrodynamics, providing improved characterization of orbital dispersion and flight path uncertainty.