scholarly journals Nonlinear model-order reduction for compressible flow solvers using the Discrete Empirical Interpolation Method

2016 ◽  
Vol 324 ◽  
pp. 194-209 ◽  
Author(s):  
Miguel Fosas de Pando ◽  
Peter J. Schmid ◽  
Denis Sipp
Keyword(s):  
Compressible Flow ◽  
Nonlinear Model ◽  
Model Order Reduction ◽  
Interpolation Method ◽  
Order Reduction ◽  
Model Order ◽  
Discrete Empirical Interpolation Method ◽  
Empirical Interpolation Method ◽  
Discrete Empirical Interpolation

A Modified Discrete Empirical Interpolation Method for Reducing Non-Linear Structural Finite Element Models

2013 ◽  
Author(s):  
Paolo Tiso ◽  
Rob Dedden ◽  
Daniel Rixen
Keyword(s):  
Finite Element ◽  
Interpolation Method ◽  
Computational Cost ◽  
Order Reduction ◽  
Galerkin Projection ◽  
Alternative Formulation ◽  
Discrete Empirical Interpolation Method ◽  
Empirical Interpolation Method ◽  
The Cost ◽  
Discrete Empirical Interpolation

Model Order Reduction (MOR) in nonlinear structural analysis problems in usually carried out by a Galerkin projection of the primary variables on a sensibly smaller space. However, the cost of computing the nonlinear terms is still of the order of the full system. The Discrete Empirical Interpolation Method (DEIM) is an effective algorithm to reduce the computational cost of the nonlinear terms of the discretized governing equations. However, its efficiency is diminished when applied to a Finite Element (FE) framework. We present here an alternative formulation of the DEIM that suits FE discretized problems and preserves the efficiency and the accuracy of the original DEIM method.

scholarly journals Efficient Wildland Fire Simulation via Nonlinear Model Order Reduction

Fluids ◽  
2021 ◽  
Vol 6 (8) ◽  
pp. 280
Author(s):  
Felix Black ◽  
Philipp Schulze ◽  
Benjamin Unger
Keyword(s):  
Model Reduction ◽  
Nonlinear Model ◽  
Interpolation Method ◽  
Wildland Fire ◽  
Classical Model ◽  
Order Reduction ◽  
Discrete Empirical Interpolation Method ◽  
Empirical Interpolation Method ◽  
Novel Method ◽  
Parameter Dependent

We propose a new hyper-reduction method for a recently introduced nonlinear model reduction framework based on dynamically transformed basis functions and especially well-suited for transport-dominated systems. Furthermore, we discuss applying this new method to a wildland fire model whose dynamics feature traveling combustion waves and local ignition and is thus challenging for classical model reduction schemes based on linear subspaces. The new hyper-reduction framework allows us to construct parameter-dependent reduced-order models (ROMs) with efficient offline/online decomposition. The numerical experiments demonstrate that the ROMs obtained by the novel method outperform those obtained by a classical approach using the proper orthogonal decomposition and the discrete empirical interpolation method in terms of run time and accuracy.

Accuracy and efficiency enhanced nonlinear model order reduction

2006 ◽  
Author(s):  
Yousof Gheisari ◽  
Hamid Reza Shaker ◽  
Mohammad Ali Torabi ◽  
Mahmoud Samavat
Keyword(s):  
Nonlinear Model ◽  
Model Order Reduction ◽  
Order Reduction ◽  
Model Order
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