Speaker
Description
Physics-based model order reduction presents a great potential for digital twins, providing highly accurate yet efficient approximations to solutions of parametrized partial differential equations. However, embedding such physics-based reduced order models in digital twin frameworks presents significant challenges. In this talk, we consider some of these challenges (as well as opportunities), particularly in the context of multi-scale materials.
In the first part, we consider model order reduction for two-scale materials simulations. Two-scale simulations are often employed to analyze the effect of the microstructure on a component’s macroscopic properties. Understanding these structure–property relations is essential in the optimal design of materials, or to enable (for example) estimation of microstructure parameters through macroscale measurements. Since these two-scale simulations are typically far too expensive computationally to use in digital twins, we explore the use of parametric MOR. To this end, we briefly present some recent work on a reduced basis methods to construct inexpensive surrogates for parametrized microscale problems, and also highlight difficulties for MOR presented by nonlinear constitutive relations in (multi-scale) problems in mechanics.
In the second part, we discuss the embedding of reduced order models in inverse and data assimilation problems, which are essential in digital twins. We will delve into strategies to mitigate the approximation error, to reduce both the offline and online computational cost, and to choose the most informative data. Finally, in conclusion, we offer insights into how we can further enhance and exploit the power of model order reduction in digital twins.
