Applications of Reduced Order and Surrogate Modeling in Structural Dynamics

Abstract

Despite recent advances in computational science, the adoption of computationally intensive, high-fidelity simulation models remains a challenge for many structural dynamics applications, especially those within the domain of uncertainty quantification (UQ), requiring repeated calls to a computationally intensive simulator. Reduced order and surrogate models offer an attractive alternative to circumvent this challenge. This contribution investigates how these modeling principles can be leveraged for different UQ applications. For both types of approximate models, the development of the corresponding (reduced order or surrogate) model is directly informed through simulations of the high-fidelity numerical model. The tuning of the approximate model aims to improve accuracy for the specific UQ task at hand, rather than targeting a globally accurate approximation. The specific applications discussed correspond to seismic loss estimation (for reduced order modeling) and posterior sampling for Bayesian inference (for surrogate modeling).