Toward alternative bounding techniques for robust goal-oriented error estimation applied to linear problems

New bounding techniques for goal-oriented error estimation applied to linear problems

Goal-oriented control of finite element models: recent advances and performances on 3D industrial applications

In this work, we present two recent developments in goaloriented error estimation applied to finite element simulations. The first one is a non-intrusive enrichment of the adjoint solution using handbook techniques, inserting locally the (generalized) Green functions associated with the quantity of interest under study. The second one is a new bounding technique, based on homothetic domains, th...

Delay-dependent robust stabilization and $H_{infty}$ control for uncertain stochastic T-S fuzzy systems with multiple time delays

In this paper, the problems of robust stabilization and$H_{infty}$ control for uncertain stochastic systems withmultiple time delays represented by the Takagi-Sugeno (T-S) fuzzymodel have been studied. By constructing a new Lyapunov-Krasovskiifunctional (LKF) and using the bounding techniques, sufficientconditions for the delay-dependent robust stabilization and $H_{infty}$ control scheme are p...

Goal-oriented Error Estimation for Free-boundary Problems Using the Exact Shape-linearized Adjoint

Since the late 1990s, goal-oriented error estimation and goal-oriented adaptive methods have been developed to control the discretization error in goal functionals of the solution1,2. These methods have mostly been applied to linear and nonlinear problems in solid and fluid mechanics. An important recent development is the extension of goal-oriented adaptive methods to multiphysics problems inv...

Robust high-dimensional semiparametric regression using optimized differencing method applied to the vitamin B2 production data

Background and purpose: By evolving science, knowledge, and technology, we deal with high-dimensional data in which the number of predictors may considerably exceed the sample size. The main problems with high-dimensional data are the estimation of the coefficients and interpretation. For high-dimension problems, classical methods are not reliable because of a large number of predictor variable...