Finite Element Models of Hyperelastic Materials Based on a New Strain Measure

To construct constitutive equations for hyperelastic materials, one increasingly often proposes new strain measures, which result in significant simplifications and error reduction in experimental data processing. One such strain measure is based on the upper triangular (QR) decomposition of the deformation gradient. We describe a finite element method for solving nonlinear elasticity problems in the framework of finite strains for the case in which the constitutive equations are written with the use of the QR-decomposition of the deformation gradient. The method permits developing an efficient, easy-to-implement tool for modeling the stress–strain state of any hyperelastic material.     

Observations on toxicologically relevant arsenic in urine in adult offspring of families with Balkan Endemic Nephropathy and controls by batch hydride generation atomic absorption spectrometry

The aim of this study was to develop a method for the characterization of internal exposure to arsenic, which is thought to play a role in the development of a kidney disease, known as Balkan Endemic Nephropathy, typical for a district in Bulgaria, and to investigate whether the As body burden differs in the offspring versus control individuals. For this case study, an analytical procedure for the determination of toxicologically relevant arsenic (the sum of arsenite, arsenate, monomethylarsonate, and dimethylarsinate) in urine by batch-type hydride generation atomic absorption spectrometry was developed. Optimization experiments for levelling off the sensitivity of inorganic arsenic and its mono- and dimethylated species in dilute HCl–L-cysteine medium were performed. The limit of detection for hydride forming arsenic fraction was 0.5?ng As, i.e. 0.25?µg?L^?1 in 10?mL of 1?+?4 v/v diluted urine. The relative standard deviation was typically 1.5–1.8% for aqueous solution and 2–6% for urine samples at 1.0?µg?L^?1 As. The sample throughput rate was 15?h^?1. No statistical correlation and cross-correlation between individuals case-control and sex at 95% confidence were found: controls (n?=?99), mean 3.5?±?2.1 (SD), range 0.9–10.4, median 3.0?µg?L^?1 As and cases (n?=?102), mean 3.6?±?2.2 (SD), range 0.5–11.0, median 3.2?µg?L^?1 As. On the basis of this study, arsenic can be excluded as a factor involved in BEN development.     

Flow injection hydride generation electrothermal atomic absorption spectrometric determination of toxicologically relevant arsenic in urine

Analytical procedure for the determination of toxicologically relevant arsenic (the sum of arsenite, arsenate, monomethylarsonate and dimethylarsinate) in urine by flow injection hydride generation and collection of generated inorganic and methylated hydrides on an integrated platform of a transverse-heated graphite atomizer for electrothermal atomic absorption spectrometric determination (ETAAS) is elaborated. Platforms are pre-treated with 2.7 μmol of zirconium and then with 0.10 μmol of iridium which serve both as an efficient hydride sequestration medium and permanent chemical modifier. Arsine, monomethylarsine and dimethylarsine are generated from diluted urine samples (10–25-fold) in the presence of 50 mmol L⁻¹ hydrochloric acid and 70 mmol L⁻¹ l-cysteine. Collection, pyrolysis and atomization temperatures are 450, 500, 2100 and 2150 °C, respectively. The characteristic mass, characteristic concentration and limit of detection (3σ) are 39 pg, 0.078 μg L⁻¹ and 0.038 μg L⁻¹ As, respectively. The limits of detection in urine are ca. 0.4 and 1 μg L⁻¹ with 10- and 25-fold dilutions. The sample throughput rate is 25 h⁻¹. Applications to several urine CRMs are given.     

Monotonicity recovering and accuracy preserving optimization methods for postprocessing finite element solutions

We suggest here a least-change correction to available finite element (FE) solution. This postprocessing procedure is aimed at recovering the monotonicity and some other important properties that may not be exhibited by the FE solution. Although our approach is presented for FEs, it admits natural extension to other numerical schemes, such as finite differences and finite volumes. For the postprocessing, a priori information about the monotonicity is assumed to be available, either for the whole domain or for a subdomain where the lost monotonicity is to be recovered. The obvious requirement is that such information is to be obtained without involving the exact solution, e.g. from expected symmetries of this solution.The postprocessing is based on solving a monotonic regression problem with some extra constraints. One of them is a linear equality-type constraint that models the conservativity requirement. The other ones are box-type constraints, and they originate from the discrete maximum principle. The resulting postprocessing problem is a large scale quadratic optimization problem. It is proved that the postprocessed FE solution preserves the accuracy of the discrete FE approximation.We introduce an algorithm for solving the postprocessing problem. It can be viewed as a dual ascent method based on the Lagrangian relaxation of the equality constraint. We justify theoretically its correctness. Its efficiency is demonstrated by the presented results of numerical experiments.     

Two splitting schemes for nonstationary convection-diffusion problems on tetrahedral meshes

Two splitting schemes are proposed for the numerical solution of three-dimensional nonstationary convection-diffusion problems on unstructured meshes in the case of a full diffusion tensor. An advantage of the first scheme is that splitting is generated by the properties of the approximation spaces and does not reduce the order of accuracy. An advantage of the second scheme is that the resulting numerical solutions are nonnegative. A numerical study is conducted to compare the splitting schemes with classical methods, such as finite elements and mixed finite elements. The numerical results show that the splitting schemes are characterized by low dissipation, high-order accuracy, and versatility.     

Commuting projections on graphs

Motivated by the increasing importance of large‐scale networks typically modeled by graphs, this paper is concerned with the development of mathematical tools for solving problems associated with the popular graph Laplacian. We exploit its mixed formulation based on its natural factorization as product of two operators. The goal is to construct a coarse version of the mixed graph Laplacian operator with the purpose to construct two‐level, and by recursion, a multilevel hierarchy of graphs and associated operators. In many situations in practice, having a coarse (i.e., reduced dimension) model that maintains some inherent features of the original large‐scale graph and respective graph Laplacian offers potential to develop efficient algorithms to analyze the underlined network modeled by this large‐scale graph. One possible application of such a hierarchy is to develop multilevel methods that have the potential to be of optimal complexity. In this paper, we consider general (connected) graphs and function spaces defined on its edges and its vertices. These two spaces are related by a discrete gradient operator, ‘Grad’ and its adjoint, ‘ ? Div’, referred to as (negative) discrete divergence. We also consider a coarse graph obtained by aggregation of vertices of the original one. Then, a coarse vertex space is identified with the subspace of piecewise constant functions over the aggregates. We consider the ?₂‐projection Q_H onto the space of these piecewise constants. In the present paper, our main result is the construction of a projection π_H from the original edge‐space onto a properly constructed coarse edge‐space associated with the edges of the coarse graph. The projections π_H and Q_H commute with the discrete divergence operator, that is, we have Div π_H = Q_H div. The respective pair of coarse edge‐space and coarse vertex‐space offer the potential to construct two‐level, and by recursion, multilevel methods for the mixed formulation of the graph Laplacian, which utilizes the discrete divergence operator. The performance of one two‐level method with overlapping Schwarz smoothing and correction based on the constructed coarse spaces for solving such mixed graph Laplacian systems is illustrated on a number of graph examples. Copyright © 2013 John Wiley & Sons, Ltd.     

Local refinement techniques for elliptic problems on cell-centered grids; II. Optimal order two-grid iterative methods

Two preconditioning techniques for solving difference equations arising in finite difference approximation of elliptic problems on cell-centered grids are studied. It is proven that the BEPS and the FAC preconditioners are spectrally equivalent to the corresponding finite difference schemes, including a nonsymmetric one, which is of higher-order accuracy. Numerical experiments that demonstrate the fast convergence of the preconditioned iterative methods (CG and GCG-LS in the nonsymmetric case) are presented.     

Variable-step multilevel preconditioning methods,I: Self-adjoint and positive definite elliptic problems

When solving large size systems of equations by preconditioned iterative solution methods, one normally uses a fixed preconditioner which may be defined by some eigenvalue information, such as in a Chebyshev iteration method. In many problems, however, it may be more effective to use variable preconditioners, in particular when the eigenvalue information is not available. In the present paper, a recursive way of constructing variable-step of, in general, nonlinear multilevel preconditioners for selfadjoint and coercive second-order elliptic problems, discretized by the finite element method is proposed. The preconditioner is constructed recursively from the coarsest to finer and finer levels. Each preconditioning step requires only block-diagonal solvers at all levels except at every k₀, k₀ ≥ 1 level where we perform a sufficient number ν, ν ≥ 1 of GCG-type variable-step iterations that involve the use again of a variable-step preconditioning for that level. It turns out that for any sufficiently large value of k₀ and, asymptotically, for ν sufficiently large, but not too large, the method has both an optimal rate of convergence and an optimal order of computational complexity, both for two and three space dimensional problem domains. The method requires no parameter estimates and the convergence results do not depend on the regularity of the elliptic problem.     

Multilevel-in-width training for deep neural network regression

A common challenge in regression is that for many problems, the degrees of freedom required for a high-quality solution also allows for overfitting. Regularization is a class of strategies that seek to restrict the range of possible solutions so as to discourage overfitting while still enabling good solutions, and different regularization strategies impose different types of restrictions. In this paper, we present a multilevel regularization strategy that constructs and trains a hierarchy of neural networks, each of which has layers that are wider versions of the previous network's layers. We draw intuition and techniques from the field of Algebraic Multigrid (AMG), traditionally used for solving linear and nonlinear systems of equations, and specifically adapt the Full Approximation Scheme (FAS) for nonlinear systems of equations to the problem of deep learning. Training through V-cycles then encourage the neural networks to build a hierarchical understanding of the problem. We refer to this approach as multilevel-in-width to distinguish from prior multilevel works which hierarchically alter the depth of neural networks. The resulting approach is a highly flexible framework that can be applied to a variety of layer types, which we demonstrate with both fully connected and convolutional layers. We experimentally show with PDE regression problems that our multilevel training approach is an effective regularizer, improving the generalize performance of the neural networks studied.     

Concise formulas for strain analysis of soft biological tissues

We describe a method for the approximate solution of nonlinear elasticity problems in the framework of finite deformation for the case of hyperelastic isotropic materials. This method enables one to write the resulting equations from the finite element method in analytical form, which reduces the amount of computations and simplifies the implementation. This approach is implemented for several types of hyperelastic materials used to describe the mechanical behavior of soft biological tissues.