A nonintrusive nonlinear model reduction method for structural dynamical problems based on machine learning

Model order reduction (MOR) has become one of the most widely used tools to create efficient surrogate models for time-critical applications. For nonlinear models, however, linear MOR approaches are only practicable to a limited extent. Nonlinear approaches, on the contrary, often require intrusive manipulations of the used simulation code. Hence, nonintrusive MOR approaches using classic model order reduction along with machine learning (ML) algorithms can provide remedy. Such approaches have drawn a lot of attention in the recent years. They rely on the idea to learn the dynamics not in a high dimensional but in a reduced space, that is, they predict the discrete sequence of reduced basis' coefficients. Open questions are the suitability of such methods in the field of structural dynamics and the best choice of the used ML algorithm. Both are addressed in this article in addition to the integration of the methodology into a modular and flexible framework that can effortless be adapted to various requirements. By applying the methodology to a dynamic mechanical system, accurate surrogate models are received, which can speed up the simulation time significantly, while still providing high-quality state approximations.

Automated summary

Model order reduction has become a widely used tool for creating efficient surrogate models for time-critical applications. Nonlinear MOR approaches often require intrusive manipulations of simulation code, while nonintrusive MOR approaches using classic model order reduction along with machine learning algorithms have gained attention in recent years. These approaches can provide accurate surrogate models for dynamic mechanical systems, significantly speeding up simulation time while maintaining high-quality state approximations.