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Parametric Model Order Reduction Using pyMOR [PDF]
, 2021 9 pages, 6 ...
Mlinarić, P., Rave, S., Saak, J.
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On the model order reduction of confined plasticity [PDF]
AIP Conference Proceedings, 2016 Forming processes usually involve irreversible plastic transformations. The calculation in that case becomes cumbersome when large parts and processes are considered. Recently Model Order Reduction techniques opened new perspectives for an accurate and fast simulation of mechanical systems. In some processes, plastic deformations remain very localized,
NASRI, Mohamed Aziz +5 more
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Efficient Wildland Fire Simulation via Nonlinear Model Order Reduction
Fluids, 2021 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.
Felix Black +2 more
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Symplectic Model Order Reduction with Non-Orthonormal Bases
Mathematical and Computational Applications, 2019 Parametric high-fidelity simulations are of interest for a wide range of applications. However, the restriction of computational resources renders such models to be inapplicable in a real-time context or in multi-query scenarios.
Patrick Buchfink +2 more
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Model Order Reduction for Nonlinear IC Models [PDF]
, 2009 Model order reduction is a mathematical technique to transform nonlinear dynamical models into smaller ones, that are easier to analyze. In this paper we demonstrate how model order reduction can be applied to nonlinear electronic circuits. First we give an introduction to this important topic. For linear time-invariant systems there exist already some
Verhoeven, A. +3 more
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Scalable Symbolic Model Order Reduction [PDF]
2008 IEEE International Behavioral Modeling and Simulation Workshop, 2008 Symbolic model order reduction (SMOR) is to reduce the complexity of a model with symbolic parameters. It is an important problem in analog circuit synthesis and digital circuit modeling with process variations. However, existing symbolic model order reduction (SMOR) methods do not scale well with the number of symbols or with the model order.
Yiyu Shi 0001 +2 more
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A New Biased Model Order Reduction for Higher Order Interval Systems
Advances in Electrical and Electronic Engineering, 2016 This paper presents a new biased method for order reduction of linear continuous time interval systems. This method is based on the Stability equation method, Pade approximation and Kharitonov’s theorem. The higher order interval system is represented by
Mangipudi Siva Kumar, Gulshad Begum
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Model order reduction based on Runge–Kutta neural networks
Data-Centric Engineering, 2021 Model order reduction (MOR) methods enable the generation of real-time-capable digital twins, with the potential to unlock various novel value streams in industry.
Qinyu Zhuang +3 more
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Model-order reduction for hyperbolic relaxation systems
International Journal of Nonlinear Sciences and Numerical Simulation, 2022 Abstract We propose a novel framework for model-order reduction of hyperbolic differential equations. The approach combines a relaxation formulation of the hyperbolic equations with a discretization using shifted base functions. Model-order reduction techniques are then applied to the resulting system of coupled ordinary differential ...
Sara Grundel, Michael Herty
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A Geometric Approach to Dynamical Model Order Reduction [PDF]
SIAM Journal on Matrix Analysis and Applications, 2018 Any model order reduced dynamical system that evolves a modal decomposition to approximate the discretized solution of a stochastic PDE can be related to a vector field tangent to the manifold of fixed rank matrices. The Dynamically Orthogonal (DO) approximation is the canonical reduced order model for which the corresponding vector field is the ...
Florian Feppon, Pierre F. J. Lermusiaux
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