A lean and efficient snapshot generation technique for the Hyper-Reduction of nonlinear structural dynamics

The established Hyper-Reduction methods require training sets which are usually obtained by a training simulation of the full, unreduced model resulting in immense offline costs. To reduce the offline-costs, so-called Nonlinear Stochastic Krylov Training Sets (NSKTS) are proposed in this paper. These training sets are obtained by solving a number of nonlinear static problems where the force is constructed by stochastically weighted forces of a Krylov force subspace. The feasibility of NSKTS as training sets for the Energy Conserving Mesh Sampling and Weighting (ECSW) Hyper-Reduction method is demonstrated on a geometrically nonlinear rubber boot example exhibiting excellent results in terms of accuracy, speedup and robustness.