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Inverse Reduced-Order Modeling
2015We propose a general probabilistic formulation of reduced-order modeling in the case the system state is hidden and characterized by some uncertainty. The objective is to integrate noisy and incomplete observations in the process of building a reduced-order model. We call this problematic inverse reduced-order modeling.
Héas, Patrick, Herzet, Cédric
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Reduced Order Models for Eigenvalue Problems
2006Two main approaches are known for the reduced order modelling of linear time-invariant systems: Krylov subspace based and SVD based approximation methods. Krylov subspace based methods have large scale applicability, but do not have a global error bound.
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Reduced order and surrogate models for gravitational waves
Living Reviews in Relativity, 2022Manuel Tiglio, Aaron Villanueva
exaly
Modelling of thermal runaway propagation in lithium-ion battery pack using reduced-order model
Energy, 2023Xuning Feng
exaly
REDUCED ORDER MODELİNG OF BRUSSELATOR MODEL
International Conference on Modern Problems of Mathematics, Mechanics and their ApplicationsAbstract. We investigate reduced order model results for the linear cross-diffusion Brusselator model. The full-order system of the Brusselator model is calculated by applying the discontinuous Galerkin method in spatial discretization and the backward Euler method in temporal discretization.
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