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Dr. Behrens-Rincon, Edwin
Nombre de publicaciĂ³n
Dr. Behrens-Rincon, Edwin
Nombre completo
Behrens Rincon, Edwin Marcelo
Facultad
Email
ebehrens@ucsc.cl
ORCID
3 results
Research Outputs
Now showing 1 - 3 of 3
- PublicationNew a posteriori error estimator for an stabilized mixed method applied to incompressible fluid flows(Applied Mathematics and Computation, 2019)
; ; GonzĂ¡lez, MarĂaWe consider an augmented mixed finite element method for incompressible fluid flows and develop a simple a posteriori error analysis. We obtain an a posteriori error estimator that is reliable and locally efficient. We provide numerical experiments that illustrate the performance of the corresponding adaptive algorithm and support its use in practice. - PublicationA note on a priori error estimates for augmented mixed methods(Applied Mathematics Letters, 2016)
; ; Bustinza, RommelIn this note we describe a strategy that improves the a priori error bounds for augmented mixed methods under appropriate hypotheses. This means that we can derive a priori error estimates for each one of the involved unknowns. Usually, the standard a priori error estimate is for the total error. Finally, a numerical example is included, that illustrates the theoretical results proven in this paper. - PublicationA posteriori error analysis of an augmented dual-mixed method in linear elasticity with mixed boundary conditions(International Journal of Numerical Analysis and Modeling, 2019)
; ; GonzĂ¡lez, MarĂaWe consider the augmented mixed finite element method introduced in [7] for the equations of plane linear elasticity with mixed boundary conditions. We develop an a posteriori error analysis based on the Ritz projection of the error and obtain an a posteriori error estimator that is reliable and efficient, but that involves a non-local term. Then, introducing an auxiliary function, we derive fully local reliable a posteriori error estimates that are locally efficient up to the elements that touch the Neumann boundary. We provide numerical experiments that illustrate the performance of the corresponding adaptive algorithm and support its use in practice.