Zobrazeno 1 - 10
of 714
pro vyhledávání: '"ROJAS, SERGIO"'
Variational Physics-Informed Neural Networks often suffer from poor convergence when using stochastic gradient-descent-based optimizers. By introducing a Least Squares solver for the weights of the last layer of the neural network, we improve the con
Externí odkaz:
http://arxiv.org/abs/2407.20417
Language identification is a crucial component in the automated production of language resources, particularly in multilingual and big data contexts. However, commonly used language identifiers struggle to differentiate between similar or closely-rel
Externí odkaz:
http://arxiv.org/abs/2404.08345
We introduce a Robust version of the Variational Physics-Informed Neural Networks method (RVPINNs). As in VPINNs, we define the quadratic loss functional in terms of a Petrov-Galerkin-type variational formulation of the PDE problem: the trial space i
Externí odkaz:
http://arxiv.org/abs/2308.16910
Leveraging Trace Theory, we investigate the efficient parallelization of direct solvers for large linear equation systems. Our focus lies on a multi-frontal algorithm, and we present a methodology for achieving near-optimal scheduling on modern massi
Externí odkaz:
http://arxiv.org/abs/2306.08994
We propose a reliable and efficient a posteriori error estimator for a hybridizable discontinuous Galerkin (HDG) discretization of the Helmholtz equation, with a first-order absorbing boundary condition, based on residual minimization. Such a residua
Externí odkaz:
http://arxiv.org/abs/2304.00418
The Adaptive Stabilized Finite Element method (AS-FEM) developed in Calo et. al. combines the idea of the residual minimization method with the inf-sup stability offered by the discontinuous Galerkin (dG) frameworks. As a result, the discretizations
Externí odkaz:
http://arxiv.org/abs/2303.17982
We introduce an adaptive superconvergent finite element method for a class of mixed formulations to solve partial differential equations involving a diffusion term. It combines a superconvergent postprocessing technique for the primal variable with a
Externí odkaz:
http://arxiv.org/abs/2210.00390
We show how to construct the deep neural network (DNN) expert to predict quasi-optimal $hp$-refinements for a given computational problem. The main idea is to train the DNN expert during executing the self-adaptive $hp$-finite element method ($hp$-FE
Externí odkaz:
http://arxiv.org/abs/2209.05844
Based on trace theory, we study efficient methods for concurrent integration of B-spline basis functions in IGA-FEM. We consider several scenarios of parallelization for two standard integration methods; the classical one and sum factorization. We ai
Externí odkaz:
http://arxiv.org/abs/2207.00280