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A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems
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.
Balanced Model Reduction via the Proper Orthogonal Decomposition
A new method for performing a balanced reduction of a high-order linear system is presented, which combines the proper orthogonal decomposition and concepts from balanced realization theory and extends to nonlinear systems.
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.…
Missing Point Estimation in Models Described by Proper Orthogonal Decomposition
- P. Astrid, S. Weiland, K. Willcox, T. Backx
- Mathematics, Computer ScienceIEEE Transactions on Automatic Control
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.
Unsteady Flow Sensing and Estimation via the Gappy Proper Orthogonal Decomposition
- K. Willcox
- 28 June 2004
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...
Model Reduction for Large-Scale Systems with High-Dimensional Parametric Input Space
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.
Data-driven operator inference for nonintrusive projection-based model reduction
Optimal Model Management for Multifidelity Monte Carlo Estimation
This work presents an optimal model management strategy that exploits multifidelity surrogate models to accelerate the estimation of statistics of outputs of computationally expensive high-fidelity models and shows that a unique analytic solution of the model management optimization problem exists under mild conditions on the models.
Localized Discrete Empirical Interpolation Method
- Benjamin Peherstorfer, D. Butnaru, K. Willcox, H. Bungartz
- Computer ScienceSIAM J. Sci. Comput.
- 11 February 2014
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 empirical…