Adjoint methods is a class of mathematical and computational techniques focused on efficiently computing the gradient of a scalar output quantity with respect to a large number of input parameters, particularly in systems governed by differential equations. This methodology involves formulating and solving an associated adjoint problem, derived from the original or 'forward' problem, which provides sensitivity information critical for gradient-based optimization, inverse problem solutions, and comprehensive sensitivity analysis across diverse scientific and engineering domains.