搜索结果: 1-13 共查到“理学 Model Reduction”相关记录13条 . 查询时间(0.555 秒)
Guaranteed Error Bounds for Model Reduction of Linear Time-Varying Systems
Time-Varying Systems Model Reduction
2015/6/19
New techniques are presented for the model reduction of linear time-varying and linear periodically-varying systems, including the formulation and proof of guaranteed upper bounds for the error. The c...
Structure-Preserving Model Reduction for Mechanical Systems
Mechanical Systems Model Reduction
2015/6/19
This paper focuses on methods of constructing of reduced-order models of mechanical systems which preserve the Lagrangian structure of the original system. These methods may be used in combination wit...
Error Bounds for Balanced Model Reduction of Linear Time-Varying Systems
Time-Varying Systems Model Reduction
2015/6/19
Error-bounds are developed for balanced truncation of linear time-varying systems, leading to an extension of the `twice the sum of the tail' formulae, well-known in the time-invariant case. The appro...
Dimensional Model Reduction in Non-Linear Finite Element Dynamics of Solids and Structures
Element Dynamics Model Reduction
2015/6/19
A general approach to the dimensional reduction of non-linear finite element models of solid dynamics is presented. For the Newmark implicit time-discretization, the computationally most expensive pha...
A Subspace Approach to Balanced Truncation for Model Reduction of Nonlinear Control Systems
Model Reduction Balanced Truncation
2015/6/19
In this paper we introduce a new method of model reduction for nonlinear control systems. Our approach is to construct an approximately balanced realization. The method requires only standard matrix c...
Empirical Model Reduction of Controlled Nonlinear Systems
Controlled Nonlinear Systems Model Reduction
2015/6/19
In this paper we introduce a new method of model reduction for nonlinear systems with inputs and outputs. The method requires only standard matrix computations, and when applied to linear systems resu...
Model Reduction for Analysis of Cascading Failures in Power Systems
Power Systems Cascading Failures
2015/6/19
In this paper, we apply a principal-orthogonal decomposition based method to the model reduction of a hybrid, nonlinear model of a power network. The results demonstrate that the sequence of fault eve...
Using model reduction to predict the soil-surface C18OO flux: an example of representing complex biogeochemical dynamics in a computationally efficient manner
Using model reduction predict the soil-surface C18OO flux representing complex biogeochemical dynamics computationally efficient manner
2014/12/16
Earth system models (ESMs) must calculate large-scale interactions between the land and atmosphere while accurately characterizing fine-scale spatial heterogeneity in water, carbon, and other nutrient...
Abstract: We introduce an interpolation framework for $\mathcal{H}_{\infty}$ model reduction founded on ideas from optimal-$\mathcal{H}_2$ interpolatory model reduction, realization theory, and comple...
Residual Minimizing Model Reduction for Parameterized Nonlinear Dynamical Systems
nonlinear dynamical systems nonlinear equations parameterized models
2011/1/17
We present a method for approximating the solution of a parameterized, nonlinear dynamical (or static) system using an affine combination of solutions computed at other points in the input parameter s...
Separation of time-scales and model reduction for stochastic reaction networks
Separation time-scales stochastic reaction networks
2010/11/12
A stochastic model for a chemical reaction network is embedded in a one-parameter family of models with species numbers and rate constants scaled by powers of the parameter. A systematic approach is ...
Judging Model Reduction of Chaotic Systems via Optimal Shadowing Criteria
Shadowing Criteria Chaotic Systems
2010/4/2
A common goal in the study of high dimensional and complex system is to model the system by a low order representation. In this letter we propose a general approach for assessing the quality of a redu...
Furthermore, we show that error output of single input single output system can be estimated over a certain class of input functions.