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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.Expand
A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems
TLDR
This paper aims to demonstrate the efforts towards in-situ applicability of EMMARM, which aims to provide real-time information about concrete mechanical properties such as E-modulus and compressive strength. Expand
Missing Point Estimation in Models Described by Proper Orthogonal Decomposition
TLDR
The effectiveness of the MPE method is demonstrated by applying it to a nonlinear computational fluid dynamic model of an industrial glass furnace and the Galerkin projection can be computed using only 25% of the spatial grid points without compromising the accuracy of the reduced model. Expand
Aerodynamic Data Reconstruction and Inverse Design Using Proper Orthogonal Decomposition
The application of proper orthogonal decomposition for incomplete (gappy) data for compressible external aerodynamic problems has been demonstrated successfully in this paper for the first time.Expand
Unsteady Flow Sensing and Estimation via the Gappy Proper Orthogonal Decomposition
Abstract The proper orthogonal decomposition (POD) has been widely used in fluid dynamic applications for extracting dominant flow features. The “gappy” POD is an extension to this method that allowsExpand
Survey of multifidelity methods in uncertainty propagation, inference, and optimization
In many situations across computational science and engineering, multiple computational models are available that describe a system of interest. These different models have varying evaluation costs...
Localized Discrete Empirical Interpolation Method
This paper presents a new approach to construct more efficient reduced-order models for nonlinear partial differential equations with proper orthogonal decomposition and the discrete empiricalExpand
Model Reduction for Large-Scale Systems with High-Dimensional Parametric Input Space
TLDR
A model-constrained adaptive sampling methodology is proposed for the reduction of large-scale systems with high-dimensional parametric input spaces using an efficient adaptive algorithm that scales well to systems with a large number of parameters. Expand
Data-driven operator inference for nonintrusive projection-based model reduction
Abstract This work presents a nonintrusive projection-based model reduction approach for full models based on time-dependent partial differential equations. Projection-based model reductionExpand
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