Stochastic dynamics of complex uncertain systems by means of a conditional - Galerkin - RBF approach

The paper presents a Galerkin/neural approach (GNa) for the dynamics analysis of nonlinear mechanical systems affected by parameter randomness. In the specialised literature various procedures are nowadays available to evaluate the response statistics of such systems, but a choice has sometimes to be done between simple methods (that often provide unreliable solutions) and other more complex methods (where accurate solutions are provided with a heavy computational effort). The proposed method, where a Galerkin approach is combined with a neural one (basically an expansion of RBF for the approximation of the system response) could be a valid alternative to the classical procedures. Furthermore the proposed Galerkin/neural approach introduces an error parameter which can provide an effective criterion to accept or refuse the obtained approximate solution. To validate the proposed approach several nonlinear systems with random parameters are introduced as case studies, and the results (main moments of the response process) are compared with Monte Carlo Simulation (MCS).