Results 231 to 240 of about 396,306 (259)

Reduced Order Modelling — Methods and Constraints

2004
There is growing attention for methods to reduce the state space dimension of a model, especially in the area of circuit simulation and electromagnetics. Applying these techniques to substructures which behave linearly or weakly non-linearly can dramatically speed up the computations in simulation of complex electronic structures.
Heres, P.J., Schilders, W.H.A.
openaire   +2 more sources

Reduced‐order modelling of an adaptive mesh ocean model

International Journal for Numerical Methods in Fluids, 2008
AbstractA novel proper orthogonal decomposition (POD) model has been developed for use with an advanced unstructured mesh finite‐element ocean model, the Imperial College Ocean Model (ICOM, described in detail below), which includes many recent developments in ocean modelling and numerical analysis.
Fang, F., Pain, C. C., Navon, I. M., Piggott, M. D., Gorman, G. J., Allison, P. A., Goddard, A. J. H.   +6 more
openaire   +3 more sources

Reduced Order Models for Eigenvalue Problems

2006
Two 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.
openaire   +2 more sources

Reduced-order Models for Coupled Problems

2010
info:eu-repo/semantics ...
Filomeno Coelho, Rajan, Xiao, Manyu, Breitkopf, Piotr, Knopf-Lenoir, Catherine, Villon, Pierre F., Sidorkiewicz, Maryan   +5 more
openaire   +2 more sources

Reduced Order Modeling

2023
Zulkeefal Dar, Joan Baiges, Ramon Codina
openaire   +1 more source

Physics-informed machine learning for reduced-order modeling of nonlinear problems

Journal of Computational Physics, 2021
Qian Wang, Chuhua Zhang, Jan Hesthaven   +2 more
exaly  

Reduced Order Modeling Methods for Aviation Noise Estimation

Sustainability, 2021
Dimitri Mavris, Ameya Behere, Tejas Puranik   +2 more
exaly  

Bayesian operator inference for data-driven reduced-order modeling

Computer Methods in Applied Mechanics and Engineering, 2022
Mengwu Guo, Shane Mcquarrie, Karen E Willcox   +2 more
exaly  

REDUCED ORDER MODELİNG OF BRUSSELATOR MODEL

International Conference on Modern Problems of Mathematics, Mechanics and their Applications
Abstract. 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.
openaire   +1 more source

Reduced order modeling for the dynamics of jointed structures through hyper-reduced interface representation

Mechanical Systems and Signal Processing, 2021
Malte Krack, Matthew Brake, Nidish Narayanaa Balaji   +2 more
exaly