Proper Generalized Decomposition model reduction in the Bayesian framework for solving inverse heat transfer problems

In this paper, the proper generalized decomposition (PGD) is used for model reduction in the solution of an inverse heat conduction problem within the Bayesian framework. Two PGD reduced order models are proposed and the approximation Error model (AEM) is applied to account for the errors between the complete and the reduced models. For the first PGD model, the direct problem solution is computed considering a separate representation of each coordinate of the problem during the process of solving the inverse problem. On the other hand, the second PGD model is based on a generalized solution integrating the unknown parameter as one of the coordinates of the decomposition. For the second PGD model, the reduced solution of the direct problem is computed before the inverse problem within the parameter space provided by the prior information about the parameters, which is required to be proper. These two reduced models are evaluated in terms of accuracy and reduction of the computational time on a transient three-dimensional two region inverse heat transfer problem. In fact, both reduced models result on substantial reduction of the computational time required for the solution of the inverse problem, and provide accurate estimates for the unknown parameter due to the application of the approximation error model approach.     

Parametric uncertainty quantification using proper generalized decomposition applied to neutron diffusion

In this paper, a proper generalized decomposition (PGD) approach is employed for uncertainty quantification purposes. The neutron diffusion equation with external sources, a diffusion-reaction problem, is used as the parametric model. The uncertainty parameters include the zone-wise constant material diffusion and reaction coefficients as well as the source strengths, yielding a large uncertain space in highly heterogeneous geometries. The PGD solution, parameterized in all uncertain variables, can then be used to compute mean, variance, and more generally probability distributions of various quantities of interest. In addition to parameterized properties, parameterized geometrical variations of three-dimensional models are also considered in this paper. To achieve and analyze a parametric PGD solution, algorithms are developed to decompose the model's parametric space and semianalytically integrate solutions for evaluating statistical moments. Varying dimensional problems are evaluated to showcase PGD's ability to solve high-dimensional problems and analyze its convergence.     

In vitro evaluation of poly(caporlactone) grafted dextran (PGD) nanoparticles with cancer cell

This study dealt with the preparation and characterization of coumarin-6 loaded poly(caprolactone) grafted dextran (PGD) nanoparticles (NPs) and evaluation of cellular uptake by using human gastric cancer cell line (SNU-638), in vitro. The potential application of these PGD NPs for sustained drug delivery was evaluated by the quantification and localization of the cellular uptake of fluorescent PGD NPs. Coumarin-6 loaded PGD NPs were prepared by a modified oil/water emulsion technique and characterized by various physico-chemical methods such as, laser light scattering for particle size and size distribution, atomic force microscopy (AFM), zeta-potential and spectrofluorometry to identify the release of fluorescent molecules from the NPs. SNU-638 was used to measure the cellular uptake of fluorescent PGD NPs. Confocal laser scanning microscopic images clearly showed the internalization of NPs by the SNU-638 cells. Cell viability was assessed by treating the SNU-638 cells with PGD NPs for 48 h. The results reveal, that these biodegradable polymeric NPs holds promise in biomedical field as a carrier.     

Local proper generalized decomposition

One of the main difficulties that a reduced‐order method could face is the poor separability of the solution. This problem is common to both a posteriori model order reduction (proper orthogonal decomposition, reduced basis) and a priori [proper generalized decomposition (PGD)] model order reduction. Early approaches to solve it include the construction of local reduced‐order models in the framework of POD. We present here an extension of local models in a PGD—and thus, a priori—context. Three different strategies are introduced to estimate the size of the different patches or regions in the solution manifold where PGD is applied. As will be noticed, no gluing or special technique is needed to deal with the resulting set of local reduced‐order models, in contrast to most proper orthogonal decomposition local approximations. The resulting method can be seen as a sort of a priori manifold learning or nonlinear dimensionality reduction technique. Examples are shown that demonstrate pros and cons of each strategy for different problems.     

Linear and nonlinear solvers for variational phase‐field models of brittle fracture

The variational approach to fracture is effective for simulating the nucleation and propagation of complex crack patterns but is computationally demanding. The model is a strongly nonlinear non‐convex variational inequality that demands the resolution of small length scales. The current standard algorithm for its solution, alternate minimization, is robust but converges slowly and demands the solution of large, ill‐conditioned linear subproblems. In this paper, we propose several advances in the numerical solution of this model that improve its computational efficiency. We reformulate alternate minimization as a nonlinear Gauss–Seidel iteration and employ over‐relaxation to accelerate its convergence; we compose this accelerated alternate minimization with Newton's method, to further reduce the time to solution, and we formulate efficient preconditioners for the solution of the linear subproblems arising in both alternate minimization and in Newton's method. We investigate the improvements in efficiency on several examples from the literature; the new solver is five to six times faster on a majority of the test cases considered. © 2016 The Authors International Journal for Numerical Methods in Engineering Published by John Wiley & Sons Ltd.     

Bayesian calculation of cost optimal burn-in test durations for mixed exponential populations

The burn-in process is a part of the production process whereby manufactured products are operated for a short period of time before release. In this paper, a Bayesian method is developed for calculating the optimal burn-in duration for a batch of products whose life distribution is modeled as a mixture of two (denoted ‘strong’ and ‘weak’) exponential sub-populations. The criteria used is the minimization of a total expected cost function reflecting costs related to the burn-in process and to product failures throughout a warranty period. The expectation is taken with respect to the mixed exponential failure model and its parameters. The prior distribution for the parameters is constructed using a beta density for the mixture parameter and independent gamma densities for the failure rate parameters of the sub-populations. It is assumed that the optimal burn-in time is selected in advance and remains fixed throughout the burn-in process. When additional failure information is available prior to the burn-in process, the minimization of posterior total cost is used as the criteria for selecting the optimal burn-in time. Expressions for the joint posterior distribution and cost are provided for the case of both complete and truncated data. The method is illustrated with an example.     

Proper generalized decomposition of a geometrically parametrized heat problem with geophysical applications

The solution of a steady thermal multiphase problem is assumed to be dependent on a set of parameters describing the geometry of the domain, the internal interfaces and the material properties. These parameters are considered as new independent variables. The problem is therefore stated in a multidimensional setup. The proper generalized decomposition (PGD) provides an approximation scheme especially well suited to preclude dramatically increasing the computational complexity with the number of dimensions. The PGD strategy is reviewed for the standard case dealing only with material parameters. Then, the ideas presented in [Ammar et al., “Parametric solutions involving geometry: A step towards efficient shape optimization.” Comput. Methods Appl. Mech. Eng., 2014; 268 :178–193] to deal with parameters describing the domain geometry are adapted to a more general case including parametrization of the location of internal interfaces. Finally, the formulation is extended to combine the two types of parameters. The proposed strategy is used to solve a problem in applied geophysics studying the temperature field in a cross section of the Earth crust subsurface. The resulting problem is in a 10-dimensional space, but the PGD solution provides a fairly accurate approximation (error ≤1%) using less that 150 terms in the PGD expansion. Copyright © 2015 John Wiley & Sons, Ltd.     

A model reduction technique based on the PGD for elastic-viscoplastic computational analysis

In this paper a model reduction approach for elastic-viscoplastic evolution problems is considered. Enhancement of the PGD reduced model by a new iterative technique involving only elastic problems is investigated and allows to reduce CPU cost. The accuracy of the solution and convergence properties are tested on an academic example and a calculation time comparison with the commercial finite element code Abaqus is presented in the case of an industrial structure.     

Monitoring a PGD solver for parametric power flow problems with goal‐oriented error assessment

The parametric analysis of electric grids requires carrying out a large number of power flow computations. The different parameters describe loading conditions and grid properties. In this framework, the proper generalized decomposition (PGD) provides a numerical solution explicitly accounting for the parametric dependence. Once the PGD solution is available, exploring the multidimensional parametric space is computationally inexpensive. The aim of this paper is to provide tools to monitor the error associated with this significant computational gain and to guarantee the quality of the PGD solution. In this case, the PGD algorithm consists in three nested loops that correspond to (1) iterating algebraic solver, (2) number of terms in the separable greedy expansion, and (3) the alternated directions for each term. In the proposed approach, the three loops are controlled by stopping criteria based on residual goal‐oriented error estimates. This allows one for using only the computational resources necessary to achieve the accuracy prescribed by the end‐user. The paper discusses how to compute the goal‐oriented error estimates. This requires linearizing the error equation and the quantity of interest to derive an efficient error representation based on an adjoint problem. The efficiency of the proposed approach is demonstrated on benchmark problems. Copyright © 2016 John Wiley & Sons, Ltd.     

Accurate calibration with highly distorted images

Camera calibration is a two-step process where first a linear algebraic approximation is followed by a nonlinear minimization. The nonlinear minimization adjusts the pin-hole and lens distortion models to the calibrating data. Since both models are coupled, nonlinear minimization can converge to a local solution easily. Moreover, nonlinear minimization is poorly conditioned since parameters with different effects in the minimization function are calculated simultaneously (some are in pixels, some in world coordinates, and some are lens distortion parameters). A local solution is adapted to parameters, which minimize the function easily, and the remaining parameters are just adapted to this solution. We propose a calibration method where traditional calibration steps are inverted. First, a nonlinear minimization is done, and after, camera parameters are computed in a linear step. Using projective geometry constraints in a nonlinear minimization process, detected point locations in the images are corrected. The pin-hole and lens distortion models are computed separately with corrected point locations. The proposed method avoids the coupling between both models. Also, the condition of nonlinear minimization increases since points coordinates are computed alone.     

Optimal spatiotemporal reduced order modeling, Part I: proposed framework

An optimal spatiotemporal reduced order modeling framework is proposed for nonlinear dynamical systems in continuum mechanics. In this paper, Part I, the governing equations for a general system are modified for an under-resolved simulation in space and time with an arbitrary discretization scheme. Basic filtering concepts are used to demonstrate the manner in which subgrid-scale dynamics arise with a coarse computational grid. Models are then developed to account for the underlying spatiotemporal structure via inclusion of statistical information into the governing equations on a multi-point stencil. These subgrid-scale models are designed to provide closure by accounting for the interactions between spatiotemporal microscales and macroscales as the system evolves. Predictions for the modified system are based upon principles of mean-square error minimization, conditional expectations and stochastic estimation, thus rendering the optimal solution with respect to the chosen resolution. Practical methods are suggested for model construction, appraisal, error measure and implementation. The companion paper, Part II, is devoted to demonstrating the methodology through a computational study of a nonlinear beam.     

不等式约束优化问题的Hestenes-Powell增广拉格朗日函数的精确性质

增广拉格朗日函数法是用无约束极小化技术求解约束优化问题的一类重要方法.本文对不等式约束优化问题的Hestenes-Powell增广拉格朗日函数(简记为HP-ALF)的精确性质作了详尽讨论.在适当的假设下,建立了原不等式约束优化问题的极小点和HP-ALF在原问题变量空间或者原问题变量空间与乘子变量空间的积空间上的无约束极小点之间的相互对应关系;获得了关于HP-ALF的精确性的许多新结果.本文给出的性质说明HP-ALF是一个连续可微的精确乘子罚函数,且用经典的乘子法可求得不等式约束优化问题的最优解和对应的拉格朗日乘子值.     

A nonintrusive proper generalized decomposition scheme with application in biomechanics

Proper generalized decomposition (PGD) is often used for multiquery and fast‐response simulations. It is a powerful tool alleviating the curse of dimensionality affecting multiparametric partial differential equations. Most implementations of PGD are intrusive extensions based on in‐house developed FE solvers. In this work, we propose a nonintrusive PGD scheme using off‐the‐shelf FE codes (such as certified commercial software) as an external solver. The scheme is implemented and monitored by in‐house flow‐control codes. A typical implementation is provided with downloadable codes. Moreover, a novel parametric separation strategy for the PGD resolution is presented. The parametric space is split into two‐ or three‐dimensional subspaces, to allow PGD technique solving problems with constrained parametric spaces, achieving higher convergence ratio. Numerical examples are provided. In particular, a practical example in biomechanics is included, with potential application to patient‐specific simulation.     

Validation of reliability computational models using Bayes networks

This paper proposes a methodology based on Bayesian statistics to assess the validity of reliability computational models when full-scale testing is not possible. Sub-module validation results are used to derive a validation measure for the overall reliability estimate. Bayes networks are used for the propagation and updating of validation information from the sub-modules to the overall model prediction. The methodology includes uncertainty in the experimental measurement, and the posterior and prior distributions of the model output are used to compute a validation metric based on Bayesian hypothesis testing. Validation of a reliability prediction model for an engine blade under high-cycle fatigue is illustrated using the proposed methodology.     

Limit analysis of masonry arches with finite compressive strength and externally bonded reinforcement

The collapse load of masonry arches with limited compressive strength and externally bonded reinforcement, such as FRP, is evaluated by solving the minimization problem obtained by applying the upper bound theorem of limit analysis. The arch is composed of a finite number of blocks. The nonlinearity of the problem (no-tension material, frictional sliding and crushing) is concentrated in the interface between two adjacent blocks. The crushing in the collapse mechanism is schematised by the interpenetration of the blocks with the formation of hinges at internal or boundary points of the interface. The minimization problem is solved with linear optimization, taking advantages of the robust algorithms offered by linear programming (LP). The optimal solution of the linear programming problem approximates the exact solution to any degree of accuracy. The dual of the minimization problem is also formulated and is solved in order to present the statics (thrust curve, locus of feasible internal reactions, etc.) of the reinforced arch as a consequence of the kinematical assumptions used in the primal minimization problem. Numerical examples are presented in order to show the effectiveness of the proposed method. Finally, it is shown that the results provided by the proposed LP are in good agreement with an experiment on a FRP-strengthened arch characterized by crushing failure of the masonry.     

Scattered data approximation by regular grid weighted smoothing

Scattered data approximation refers to the computation of a multi-dimensional function from measurements obtained from scattered spatial locations. For this problem, the class of methods that adopt a roughness minimization are the best performing ones. These methods are called variational methods and they are capable of handling contrasting levels of sample density. These methods express the required solution as a continuous model containing a weighted sum of thin-plate spline or radial basis functions with centres aligned to the measurement locations, and the weights are specified by a linear system of equations. The main hurdle in this type of method is that the linear system is ill-conditioned. Further, getting the weights that are parameters of the continuous model representing the solution is only a part of the effort. Getting a regular grid image requires re-sampling of the continuous model, which is typically expensive. We develop a computationally efficient and numerically stable method based on roughness minimization. The method leads to an algorithm that uses standard regular grid array operations only, which makes it attractive for parallelization. We demonstrate experimentally that we get these computational advantages only with a little compromise in performance when compared with thin-plate spline methods.     

Limit analysis of masonry arches with finite compressive strength and externally bonded reinforcement

The collapse load of masonry arches with limited compressive strength and externally bonded reinforcement, such as FRP, is evaluated by solving the minimization problem obtained by applying the upper bound theorem of limit analysis. The arch is composed of a finite number of blocks. The nonlinearity of the problem (no-tension material, frictional sliding and crushing) is concentrated in the interface between two adjacent blocks. The crushing in the collapse mechanism is schematised by the interpenetration of the blocks with the formation of hinges at internal or boundary points of the interface. The minimization problem is solved with linear optimization, taking advantages of the robust algorithms offered by linear programming (LP). The optimal solution of the linear programming problem approximates the exact solution to any degree of accuracy. The dual of the minimization problem is also formulated and is solved in order to present the statics (thrust curve, locus of feasible internal reactions, etc.) of the reinforced arch as a consequence of the kinematical assumptions used in the primal minimization problem. Numerical examples are presented in order to show the effectiveness of the proposed method. Finally, it is shown that the results provided by the proposed LP are in good agreement with an experiment on a FRP-strengthened arch characterized by crushing failure of the masonry.     

Solution to the topology optimization problem using a time-evolution equation

This article describes a numerical solution to the topology optimization problem using a time-evolution equation. The design variables of the topology optimization problem are defined as a mathematical scalar function in a given design domain. The scalar function is projected to the normalized density function. The adjoint variable method is used to determine the gradient defined as the ratio of the variation of the objective function or constraint function to the variation of the design variable. The variation of design variables is obtained using the solution of the time-evolution equation in which the source term and Neumann boundary condition are given as a negative gradient. The distribution of design variables yielding an optimal solution is obtained by time integration of the solution of the time-evolution equation. By solving the topology optimization problem using the proposed method, it is shown that the objective function decreases when the constraints are satisfied. Furthermore, we apply the proposed method to the thermal resistance minimization problem under the total volume constraint and the mean compliance minimization problem under the total volume constraint.     

Material characterization of laminated composite plates via static testing

A minimization method for material characterization of laminated composite plates using static test results is presented. Mechanical responses such as strains and displacements are measured from the static tests of the laminated composite plates. The finite element method is used to analyse the deformation of the laminated composite plates. An error function is established to measure the differences between the experimental and theoretical mechanical responses of the laminated composite plates. The identification of the material elastic constants of the laminated composite plates is formulated as a constrained minimization problem in which the elastic constants are determined by making the error function a global minimum. A number of examples are given to illustrate the feasibility and applications of the proposed method.