Results 31 to 40 of about 702,196 (224)

On the Peculiarities of Solving the Coefficient Inverse Problem of Heat Conduction for a Two-Part Layer [PDF]

Известия Саратовского университета. Новая серия: Математика. Механика. Информатика, 2019
The coefficient inverse problem of thermal conductivity about the determination of the thermophysical characteristics of the functional-gradient part of a two-component layer is posed.
Vatulyan, Alexandr Ovanesovitsch, Nesterov, Sergei Anatolevich   +1 more
doaj   +1 more source

Model order reduction with Galerkin projection applied to nonlinear optimization with infeasible primal‐dual interior point method

International Journal for Numerical Methods in Engineering, 2019
It is not new that model order reduction (MOR) methods are employed in almost all fields of engineering to reduce the processing time of complex computational simulations.
P. Nigro, E. Simões, P. Pimenta, J. Schröder   +3 more
semanticscholar   +1 more source

To be or not to be intrusive? The solution of parametric and stochastic equations - the "plain vanilla" Galerkin case [PDF]

, 2013
In parametric equations - stochastic equations are a special case - one may want to approximate the solution such that it is easy to evaluate its dependence of the parameters.
Giraldi, Loïc, Litvinenko, Alexander, Liu, Dishi, Matthies, Hermann G., Matthies, Hermann,, Nouy, Anthony   +5 more
core   +4 more sources

BUQEYE guide to projection-based emulators in nuclear physics

Frontiers in Physics, 2023
The BUQEYE collaboration (Bayesian Uncertainty Quantification: Errors in Your effective field theory) presents a pedagogical introduction to projection-based, reduced-order emulators for applications in low-energy nuclear physics.
C. Drischler, C. Drischler, J. A. Melendez, R. J. Furnstahl, A. J. Garcia, Xilin Zhang   +5 more
doaj   +1 more source

Uncertainty quantification for nonlinear difference equations with dependent random inputs via a stochastic Galerkin projection technique

Communications in nonlinear science & numerical simulation, 2019
Discrete stochastic systems model discrete response data on some phenomenon with inherent uncertainty. The main goal of uncertainty quantification is to derive the probabilistic features of the stochastic system.
J. Calatayud, J. Cortés, M. Jornet
semanticscholar   +1 more source

Local convergence of the FEM for the integral fractional Laplacian

, 2020
We provide for first order discretizations of the integral fractional Laplacian sharp local error estimates on proper subdomains in both the local $H^1$-norm and the localized energy norm.
Faustmann, Markus, Karkulik, Michael, Melenk, Jens Markus   +2 more
core   +2 more sources

Sample-based and sample-aggregated based Galerkin projection schemes for structural dynamics

Probabilistic Engineering Mechanics, 2017
A comparative study of two new Galerkin projection schemes to compute the response of discretised stochastic partial differential equations is presented for discretised structures subjected to static and dynamic loads.
S. E. Pryse, S. Adhikari, A. Kundu
semanticscholar   +1 more source

Accelerating CFD via local POD plus Galerkin projections

, 2022
Se desarrollan varias técnicas basadas en descomposición ortogonal propia (DOP) local y proyección de tipo Galerkin para acelerar la integración numérica de problemas de evolución, de tipo parabólico, no lineales. Las ideas y métodos que se presentan conllevan un nuevo enfoque para la modelización de tipo DOP, que combina intervalos temporales cortos ...
openaire   +3 more sources

Variational Multiscale Stabilization and the Exponential Decay of Fine-scale Correctors [PDF]

, 2015
This paper addresses the variational multiscale stabilization of standard finite element methods for linear partial differential equations that exhibit multiscale features. The stabilization is of Petrov-Galerkin type with a standard finite element trial
A. Abdulle, A. Abdulle, A. Målqvist, A. Målqvist, A. Målqvist, C. Carstensen, C. Makridakis, D. Elfverson, D. Elfverson, D. Elfverson, D. Gallistl, D. Peterseim, D.B. Szyld, G. Rozza, H. Owhadi, H. Wu, I. Babuska, I. Babuška, J.A. Cottrell, J.A. Cottrell, J.M. Melenk, J.M. Melenk, J.M. Melenk, L. Demkowicz, L. Grasedyck, L.R. Scott, M.G. Larson, M.G. Larson, O. Cessenat, P. Cummings, P. Henning, P. Henning, P. Henning, P. Henning, R. Hiptmair, R. Tezaur, T. Betcke, T. Hughes, T.J.R. Hughes, T.J.R. Hughes, U. Hetmaniuk, X. Feng   +41 more
core   +2 more sources

Breaking the Kolmogorov Barrier in Model Reduction of Fluid Flows

Fluids, 2020
Turbulence modeling has been always a challenge, given the degree of underlying spatial and temporal complexity. In this paper, we propose the use of a partitioned reduced order modeling (ROM) approach for efficient and effective approximation of ...
Shady E. Ahmed, Omer San
doaj   +1 more source