Data-driven model order reduction for wave propagation in materials with damage and nonlinearities
This work addresses computational efficiency for materials science simulations, but it is incremental as it applies existing reduction techniques to a specific domain.
The paper tackles the challenge of simulating wave propagation in materials with nonlinearities or damage by applying model order reduction methods to accelerate high-dimensional computations, and it evaluates their performance in three numerical examples.
In this work, we consider wave propagation in materials characterized by nonlinear properties or damage. To accelerate the simulations of the resulting high-dimensional problems, we apply model order reduction methods. Depending on the knowledge of the underlying equations and the availability of their discrete operators, intrusive methods (here projection-based approaches based on proper orthogonal decomposition (POD)) or non-instrusive methods (here data-driven approaches including dynamic mode decomposition (DMD) and operator inference (OpInf)) can be used. We recall the theoretical foundations of the methods and apply them to the problem of wave propagation. In three different numerical examples, we evaluate the performance of the reduction techniques.