Model Order Reduction
In order to get a deep insight into the underlying process, dynamics, structure or devices, modeling and simulation are unavoidable in many research and application fields. The resulting mathematical models are usually in the form of partial differential equations. To simulate such models, spatial (-time) discretization is necessary, which results in large-scale, complex systems with enormous number of equations. The simulation becomes time-consuming because of the large scale and complexity of the systems.
Model order reduction (MOR), developed from well-established mathematical theories, robust numerical algorithms, and aided by machine learning, has been recognized as an efficient tool for fast simulation.
