Efficient Adaptive Elimination Strategies in Nonlinear FETI-DP Methods in Combination with Adaptive Spectral Coarse Spaces

Nonlinear FETI-DP (Finite Element Tearing and Interconnecting - Dual Primal) is a nonlinear nonoverlapping domain decomposition method (DDM) which has a superior nonlinear convergence behavior compared with classical Newton-Krylov-DDMs - at least for many problems. Its fast and robust nonlinear convergence is strongly influenced by the choice of the second level or, in other words, the choice of the coarse constraints. Additionally, the convergence is also affected by the choice of an elimination set, that is, a set of degrees of freedom which are eliminated nonlinearly before linearization. In this article, an adaptive coarse space is combined with a problem-dependent and residual-based choice of the elimination set. An efficient implementation exploiting sparse local saddle point problems instead of an explicit transformation of basis is used. Unfortunately, this approach makes a further adaption of the elimination sets necessary, that is, edges and faces with coarse constraints have to be either included in the elimination set completely or not at all. Different strategies to fulfill this additional constraint are discussed and compared with a solely residual-based approach. The latter approach has to be implemented with an explicit transformation of basis. In general, the residual which is used to choose the elimination set has to be transformed to a space which basis functions explicitly contain the coarse constraints. This is computationally expensive. Here, for the first time, it is suggested to use an approximation of the transformed residual instead to compute the elimination set

Macroeconomic resilience in a DSGE model

We use the dynamic stochastic general equilibrium (DSGE) model of Altig et al. (2005) to analyse the resilience of an economy in the face of external shocks. The term resilience refers to the ability of an economy to prosper in the face of shocks. The Altig et al. model was chosen because it combined both demand and supply shocks and because various market rigidities/imperfections, which have the potential to affect resilience, are modelled. We consider the level of expected discounted utility to be the relevant measure of resilience. The effect of market rigidities, eg. wage and price stickiness, on the expected level of utility is minimal. The effect on utility is especially small when compared to the effect of market competition, because the latter has a direct effect on the level of output. This conclusion holds for the family of constant-relative-risk-aversion-over-consumption utility functions. A similar conclusion was drawn by Lucas (1987) regarding the costs of business cycles. We refer to the literature that followed Lucas for ideas for how a DSGE model might be adjusted to give a more meaningful analysis of resilience. We conclude that the Altig et al. DSGE model does not produce a relationship between rigidities and the level of output and, hence, does not capture the effect of inflexibility on utility that one observes colloquially.

Learning Adaptive FETI-DP Constraints for Irregular Domain Decompositions

Adaptive coarse spaces yield a robust convergence behavior for FETI-DP (Finite Element Tearing and Interconnecting - Dual Primal) and BDDC (Balancing Domain Decomposition by Constraints) methods for highly heterogeneous problems. However, the usage of such adaptive coarse spaces can be computationally expensive since, in general, it requires the setup and the solution of a relatively high amount of local eigenvalue problems on parts of the domain decomposition interface. In earlier works, see, e.g., [2], it has been shown that it is possible to train a neural network to make an automatic decision which of the eigenvalue problems in an adaptive FETI-DP method are actually necessary for robustness with a satisfactory accuracy. Moreover, these results have been extended in [6] by directly learning an approximation of the adaptive edge constraints themselves for regular, two-dimensional domain decompositions. In particular, this does not require the setup or the solution of any eigenvalue problems at all since the FETI-DP coarse space is, in this case, exclusively enhanced by the learned constraints obtained from the regression neural networks trained in an offline phase. Here, in contrast to [6], a regression neural network is trained with both, training data resulting from straight and irregular edges. Thus, it is possible to use the trained networks also for the approximation of adaptive constraints for irregular domain decompositions. Numerical results for a heterogeneous two-dimensional stationary diffusion problem are presented using both, a decomposition into regular and irregular subdomains

Three-level BDDC for Virtual Elements

The Virtual Element Method (VEM) is a discretization procedure for the solution of partial differential equations that allows for the use of nearly arbitrary polygonal/polyhedral grids. For the parallel scalable and iterative solution of large scale VE problems, the FETI-DP (Finite Element Tearing and Interconnecting - Dual Primal) and BDDC (Balancing Domain Decomposition by Constraints) domain decomposition methods have recently been applied. As for the case of finite element discretizations, a large global coarse problem that usually arises in large scale applications is a parallel scaling bottleneck of FETI-DP and BDDC. Nonetheless, the coarse problem/second level is usually necessary for the numerical robustness of the method. To alleviate this difficulty and to retain the scalability, the three-level BDDC method is applied to virtual element discretizations in this article. In this approach, to allow for a parallel solution of the coarse problem, the solution of it is only approximated by applying BDDC recursively, which automatically introduces a third level. Numerical results using several different configurations of the three-level approach and different polygonal meshes are presented and additionally compared with the classical two-level BDDC approach

Adaptive Three-level BDDC Using Frugal Constraints

The highly parallel scalable three-level BDDC (Balancing Domain Decomposition by Constraints) method is a very successful approach to overcome the scaling bottleneck of directly solving a large coarse problem in classical two-level BDDC. As long as the problem is homogeneous on each subregion, three-level BDDC is also provably robust in many cases. For problems with complex microstructures, as, e.g., stationary diffusion problems with jumps in the diffusion coefficient function, in two-level BDDC methods, advanced adaptive or frugal coarse spaces have to be considered to obtain a robust preconditioner. Unfortunately, these approaches result in even larger coarse problems on the second level and, additionally, computing adaptive coarse constraints is computationally expensive. Therefore, in this article, the three-level approach is combined with a provably robust adaptive coarse space and the computationally cheaper frugal coarse space. Both coarse spaces are used on the second as well as the third level. All different possible combinations are investigated numerically for stationary diffusion problems with highly varying coefficient functions

Adaptive Nonlinear Elimination in Nonlinear FETI-DP Methods

Highly scalable and robust Newton-Krylov domain decomposition approaches are widely used for the solution of nonlinear implicit problems. In these methods, the nonlinear problem is first linearized and then decomposed into subdomains. By changing this order and first decomposing the nonlinear problem, many nonlinear domain decomposition methods have been designed in the last two decades. These methods often show a higher robustness compared to classical Newton-Krylov variants due to a better resolution of nonlinear effects. Additionally, the balance between local work, communication, and synchronization is usually more favorable for modern computer architectures. In all our nonlinear FETI-DP methods, we introduce a nonlinear right-preconditioner that can be interpreted as a (partial) nonlinear elimination of variables. The choice of the elimination set has a huge impact on the nonlinear convergence behavior. In order to design a nonlinear FETI-DP method that is tailored to arbitrary problems, we introduce a strategy, based on the residual of the nonlinear saddle point system, to adaptively choose sets of variables for the nonlinear elimination. The new strategy is applied to challenging distributions of nonlinearity in problems based on the p-Laplace operator. Promising numerical results are presented

Nonlinear FETI-DP and BDDC Methods

In the simulation of deformation processes in material science the consideration of a microscopic material structure is often necessary, as in the simulation of modern high strength steels. A straightforward finite element discretization of the complete deformed body resolving the microscopic structure leads to very large nonlinear problems and a solution is out of reach, even on modern supercomputers. In homogenization approaches, as the computational scale bridging approach FE2, the macroscopic scale of the deformed object is decoupled from the microscopic scale of the material structure. These approaches only consider the microstructure in a localized fashion on independent and parallel representative volume elements (RVEs). This introduces massive parallelism on the macroscopic level and is thus ideal for modern computer architectures with large numbers of parallel computational cores. Nevertheless, the discretization of an RVE can still result in large nonlinear problems and thus highly scalable parallel solvers are necessary. In this context, nonlinear FETI-DP (Finite Element Tearing and Interconnecting - Dual-Primal) and BDDC (Balancing Domain Decomposition by Constraints) domain decomposition methods are discussed in this thesis, which are parallel solution methods for nonlinear problems arising from a finite element discretization. These approaches can be viewed as a strategies to further localize the computational work and to extend the parallel scalability of classical FETI-DP and BDDC methods towards extreme-scale supercomputers. Also variants providing an inexact solution of the FETI-DP coarse problem are considered in this thesis, combining two successful paradigms, i.e., nonlinear domain decomposition and AMG (Algebraic Multigrid). An efficient implementation of the resulting inexact reduced Nonlinear-FETI-DP-1 method is presented and scalability beyond 200,000 computational cores is showed. Finally, a highly scalable FE2 implementation using recent inexact reduced FETI-DP methods to solve the RVE problems on the microscopic level is presented and scalability on all 458,752 cores of the JUQUEEN BlueGene/Q system at Forschungszentrum Jülich is demonstrated

Computational homogenization for aerogel-like polydisperse open-porous materials using neural network--based surrogate models on the microscale

The morphology of nanostructured materials exhibiting a polydisperse porous space, such as aerogels, is very open porous and fine grained. Therefore, a simulation of the deformation of a large aerogel structure resolving the nanostructure would be extremely expensive. Thus, multi-scale or homogenization approaches have to be considered. Here, a computational scale bridging approach based on the FE$^2$ method is suggested, where the macroscopic scale is discretized using finite elements while the microstructure of the open-porous material is resolved as a network of Euler-Bernoulli beams. Here, the beam frame based RVEs (representative volume elements) have pores whose size distribution follows the measured values for a specific material. This is a well-known approach to model aerogel structures. For the computational homogenization, an approach to average the first Piola-Kirchhoff stresses in a beam frame by neglecting rotational moments is suggested. To further overcome the computationally most expensive part in the homogenization method, that is, solving the RVEs and averaging their stress fields, a surrogate model is introduced based on neural networks. The networks input is the localized deformation gradient on the macroscopic scale and its output is the averaged stress for the specific material. It is trained on data generated by the beam frame based approach. The effiency and robustness of both homogenization approaches is shown numerically, the approximation properties of the surrogate model is verified for different macroscopic problems and discretizations. Different (Quasi-)Newton solvers are considered on the macroscopic scale and compared with respect to their convergence properties

Predicting the geometric location of critical edges in adaptive GDSW overlapping domain decomposition methods using deep learning

Overlapping GDSW domain decomposition methods are considered for diffusion problems in two dimensions discretized by finite elements. For a diffusion coefficient with high contrast, the condition number is usually dependent on it. A remedy is given by adaptive domain decomposition methods, where the coarse space is enhanced by additional coarse basis functions. These are chosen problem-dependently by solving small local eigenvalue problems. Here, the eigenvalue problems (EVPs) are associated with the edges of the domain decomposition interface; edges, where these EVPs have to be solved are denoted as critical edges. For many applications, not all edges are critical and the solution of the EVPs is not necessary. In an earlier work, a strategy to predict the location of critical edges, based on deep learning, has been proposed for adaptive FETI-DP, a class of nonoverlapping methods. In the present work, this strategy is successfully applied to adaptive GDSW; differences in the classification process for this overlapping method are described. Choosing the classification threshold has been a challenge in all these approaches. Here, for the first time, a heuristic based on the receiver operating characteristic (ROC) curve and the precision-recall graph is discussed. Results for a challenging realistic coefficient function are presented

Combining Machine Learning and Domain Decomposition Methods – A Review

Scientific machine learning, an area of research where techniques from machine learning and scientific computing are combined, has become of increasing importance and receives growing attention. Here, our focus is on a very specific area within scientific machine learning given by the combination of domain decomposition methods with machine learning techniques. The aim of the present work is to make an attempt of providing a review of existing and also new approaches within this field as well as to present some known results in a unified framework; no claim of completeness is made. As a concrete example of machine learning enhanced domain decomposition methods, an approach is presented which uses neural networks to reduce the computational effort in adaptive domain decomposition methods while retaining their robustness. More precisely, deep neural networks are used to predict the geometric location of constraints which are needed to define a robust coarse space. Additionally, two recently published deep domain decomposition approaches are presented in a unified framework. Both approaches use physics-constrained neural networks to replace the discretization and solution of the subdomain problems of a given decomposition of the computational domain. Finally, a brief overview is given of several further approaches which combine machine learning with ideas from domain decomposition methods to either increase the performance of already existing algorithms or to create completely new methods