On understanding the relationship between structure in the potential surface and observables in classical dynamics: A functional sensitivity analysis approach

The relationship between structure in the potential surface and classical mechanical observables is examined by means of functional sensitivity analysis. Functional sensitivities provide maps of the potential surface, highlighting those regions that play the greatest role in determining the behavior of observables. A set of differential equations for the sensitivities of the trajectory components are derived. These are then solved using a Green's function method. It is found that the sensitivities become singular at the trajectory turning points with the singularities going as η^-3/2, with η being the distance from the nearest turning point. The sensitivities are zero outside of the energetically and dynamically allowed region of phase space. A second set of equations is derived from which the sensitivities of observables can be directly calculated. An adjoint Green's function technique is employed, providing an efficient method for numerically calculating these quantities. Sensitivity maps are presented for a simple collinear atom-diatom inelastic scattering problem and for two Henon-Heiles type Hamiltonians modeling intramolecular processes. It is found that the positions of the trajectory caustics in the bound state problem determine regions of the highest potential surface sensitivities. In the scattering problem (which is impulsive, so that "sticky" collisions did not occur), the positions of the turning points of the individual trajectory components determine the regions of high sensitivity. In both cases, these lines of singularities are superimposed on a rich background structure. Most interesting is the appearance of classical interference effects. The interference features in the sensitivity maps occur most noticeably where two or more lines of turning points cross. The important practical motivation for calculating the sensitivities derives from the fact that the potential is a function, implying that any direct attempt to understand how local potential regions affect the behavior of the observables by repeatedly and systematically altering the potential will be prohibitively expensive. The functional sensitivity method enables one to perform this analysis at a fraction of the computational labor required for the direct method.