Now showing items 1-6 of 6
Deep learning driven self-adaptive hp finite element method
The fi nite element method (FEM) is a popular tool for solving engineering problems governed by Partial Di fferential Equations (PDEs). The accuracy of the numerical solution depends on the quality of the computational ...
A Finite Element based Deep Learning solver for parametric PDEs
We introduce a dynamic Deep Learning (DL) architecture based on the Finite Element Method (FEM) to solve linear parametric Partial Differential Equations(PDEs). The connections between neurons in the architecture mimic the ...
Sensitivity and Uncertainty Analysis by Discontinuous Galerkin of Lock-in Thermography for Crack Characterization
This work focuses on the characterization of narrow vertical cracks of nite size using optically excited lock-in thermography (OLT). To characterize these cracks, we need to solve an ill-posed inverse problem. As a previous ...
Forward-in-Time Goal-Oriented Adaptivity
In goal-oriented adaptive algorithms for partial differential equations, we adapt the finite element mesh in order to reduce the error of the solution in some quantity of interest. In time-dependent problems, this adaptive ...
Time-Domain Goal-Oriented Adaptivity Using Pseudo-Dual Error Representations
Goal-oriented adaptive algorithms produce optimal grids to solve challenging engineering problems. Recently, a novel error representation using (unconventional) pseudo-dual problems for goal-oriented adaptivity in the ...
Goal-Oriented p-Adaptivity using Unconventional Error Representations for a 1D Steady State Convection-Diffusion Problem
This work proposes the use of an alternative error representation for Goal-Oriented Adaptivity (GOA) in context of steady state convection dominated diffusion problems. It introduces an arbitrary operator for the computation ...