Results 31 to 40 of about 24,860,274 (294)
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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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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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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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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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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Model Order Reduction of Non-Linear Magnetostatic Problems Based on POD and DEI Methods [PDF]
, 2014 In the domain of numerical computation, Model Order Reduction approaches are more and more frequently applied in mechanics and have shown their efficiency in terms of reduction of computation time and memory storage requirements. One of these approaches,
CLENET, Stephane, HENNERON, Thomas
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Model order reduction assisted by deep neural networks (ROM-net)
Advanced Modeling and Simulation in Engineering Sciences, 2020 In this paper, we propose a general framework for projection-based model order reduction assisted by deep neural networks. The proposed methodology, called ROM-net, consists in using deep learning techniques to adapt the reduced-order model to a ...
Thomas Daniel +3 more
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Model order reduction for nonlinear Schrödinger equation [PDF]
Applied Mathematics and Computation, 2015 We apply the proper orthogonal decomposition (POD) to the nonlinear Schrödinger (NLS) equation to derive a reduced order model. The NLS equation is discretized in space by finite differences and is solved in time by structure preserving symplectic mid-point rule. A priori error estimates are derived for the POD reduced dynamical system.
Bülent Karasözen +2 more
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Model order reduction applied to the numerical study of electrical motor based on POD method taking into account rotation movement [PDF]
, 2014 In order to reduce the computation time and the memory resources required to solve an electromagnetic field problem, Model Order Reduction (MOR) approaches can be applied to reduce the size of the linear equation system obtained after discretisation.
CLENET, Stephane, HENNERON, Thomas
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Model order reduction for optimality systems through empirical gramians
Frontiers in Applied Mathematics and Statistics, 2023 In the present article, optimal control problems for linear parabolic partial differential equations (PDEs) with time-dependent coefficient functions are considered.
Luca Mechelli +2 more
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