Results 11 to 20 of about 10,310 (200)

Artificial Neural Network and Data Dimensionality Reduction Based on Machine Learning Methods for PMSM Model Order Reduction

IEEE Access, 2021
The present paper targets a solution for permanent motor synchronous machine (PMSM) model order reduction (MOR) using artificial neural networks and machine learning techniques for data dimensionality reduction. The neural networks are trained using data
Maria Raluca Raia, Mircea Ruba, Raul Octavian Nemes, Claudia Martis   +3 more
doaj   +1 more source

A dual reduction strategy for reduce-order modeling of periodic control system

Results in Control and Optimization, 2021
Model order reduction (MOR) of periodic systems using the Krylov subspace methods received lots of interest in last few decades. In this paper, a structured Krylov subspace based model reduction for linear discrete-time periodic (LDTP) control system has
Mohammad-Sahadet Hossain, Muhtasim Alam Chowdhury, Md. Samiur Rahman   +2 more
doaj   +1 more source

Piezoelectric Beam Finite Element Model and Its Reduction and Control

Journal of Mechanical Engineering, 2021
The paper deals with the development of the finite element method (FEM) model of piezoelectric beam elements, where the piezoelectric layers are located on the outer surfaces of the beam core, which is made of functionally graded material.
Vladimír Kutiš, Juraj Paulech, Gálik Gálik, Justín Murín   +3 more
doaj   +1 more source

Model Order Reduction of Large Scale ODE Systems: MOR for ANSYS versus ROM Workbench [PDF]

, 2007
In this paper we compare the numerical results obtained by different model order reduction software tools, in order to test their scalability for relevant problems of the microelectronic-industry. MOR for ANSYS is implemented in C++ and ROMWorkbench is a MATLAB code.We further compare two Arnoldi-based reduction algorithms, which seems to be the most ...
Vollebregt, A.J., Bechtold, T., Verhoeven, A., Maten, ter, E.J.W.   +3 more
openaire   +3 more sources

An Algorithmic Comparison of the Hyper-Reduction and the Discrete Empirical Interpolation Method for a Nonlinear Thermal Problem

Mathematical and Computational Applications, 2018
A novel algorithmic discussion of the methodological and numerical differences of competing parametric model reduction techniques for nonlinear problems is presented.
Felix Fritzen, Bernard Haasdonk, David Ryckelynck, Sebastian Schöps   +3 more
doaj   +1 more source

An Improved Structure-Preserving Reduced-Order Interconnect Macromodeling for Large-Scale Equation Sets of Transient Interconnect Circuit Problems

IEEE Access, 2021
The widely applied Structure-Preserving Reduced-order Interconnect Macromodeling (SPRIM) algorithm cannot retain the reciprocity or the block structure of the circuit matrices of the original system, being inherent to RCL circuits; and consequently it is
Ning Wang, Shiyou Yang, Zhuoxiang Ren, Huifang Wang   +3 more
doaj   +1 more source

Model Order Reduction (MOR) of Function‐Perfusion‐Growth Simulation in the Human Fatty Liver via Artificial Neural Network (ANN) [PDF]

PAMM, 2019
AbstractNumerical modeling of biological systems has become an important assistance for understanding and predicting hepatic diseases like non‐alcoholic fatty liver disease (NAFLD) or the detoxification of drugs and toxines by the liver. We developed a model for the simulation of hepatic function‐perfusion processes using a multiscale and multiphase ...
Lena Lambers, Tim Ricken, Matthias König   +2 more
openaire   +1 more source

An improved whale optimization algorithm for the model order reduction of large-scale systems

Journal of Electrical Systems and Information Technology, 2023
An improved whale optimization algorithm (IWOA) is developed for the model order reduction (MOR) of large-scale systems (LSS) in this paper. An equivalent reduced order model (ROM) for the higher-order system (HOS) is derived by considering integral ...
Dasu Butti, Sivakumar Mangipudi, Srinivasarao Rayapudi, Madhu Kiran Buddi, Kalyan Raj Kaniganti, Trinayani Chittajallu, Bhavana Rushi Dontu, Vijay Arun Dunna, RamaKrishna Boni, Chenchu Kishore Kumar Kothuri   +9 more
doaj   +1 more source

An Adaptive Model Order Reduction Method Based on the Damage Evolution for Nonlinear Seismic Analysis

Advances in Civil Engineering, 2020
Nonlinear seismic analysis, an approach to evaluate the seismic performance of a structure, is facing the challenge of computational efficiency for large-scale and high-fidelity simulation.
Jian Wang, Ming Fang, Hui Li
doaj   +1 more source

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, Ashish Bhatt, Bernard Haasdonk   +2 more
doaj   +1 more source