SCR-Filter Model Order Reduction (2): Proper Orthogonal Decomposition and Artificial Neural Network

Proper Orthogonal Decomposition Model Order Reduction of nonlinear IC models

Future simulation for nanoelectronics requires that circuit equations can be coupled to electromagnetics, to semiconductor equations, and to heat transfer. The consequence is that one has to deal with large systems. Model Order Reduction (MOR) is a means to speed up simulation of large systems. Existing MOR techniques mostly apply to linear problems and even then they have to be generalized to ...

Model Reduction via Proper Orthogonal Decomposition

Model Reduction by Proper Orthogonal Decomposition (POD)

Mathematical models for human tissue and blood flow both represent time dependent nonlinear partial differential equations in three space dimensions. Their numerical solution based on appropriate space/time discretizations requires computational times that even when using state-of-the-art algorithmic solvers are far from being acceptable for real time OR scenarios. A way to overcome this diffic...

Artificial viscosity proper orthogonal decomposition

We introduce improved reduced-order models for turbulent flows. These models are inspired from successful methodologies used in large eddy simulation, such as artificial viscosity, applied to standardmodels created by proper orthogonal decomposition of flows coupled with Galerkin projection. As a first step in the analysis and testing of our new methodology, we use the Burgers equation with a s...

Balanced Model Reduction via the Proper Orthogonal Decomposition

A new method for performing a balanced reduction of a high-order linear system is presented. The technique combines the proper orthogonal decomposition and concepts from balanced realization theory. The method of snapshots is used to obtain low-rank, reduced-rangeapproximationsto the system controllabilityand observability grammiansin either the timeor frequency domain.The approximationsare the...