Variational Inequalities

Many problems in physical and engineering sciences are modeled by partial differential equations (PDEs). However, various more complex physical processes are described by variational inequalities (VIs). Variational inequalities form an important and very useful class of nonlinear problems arising in diverse application areas of physical, engineering, financial, and management sciences.

Discontinuous Galerkin (DG) methods have been widely used for solving a variety of mathematical and physical problems due to the advantage of capturing non-smooth or oscillatory solutions effectively and the flexibility in constructing feasible local shape function spaces. Because of their discontinuous nature, DG methods are very suitable for hp-adaptive algorithms. Another advantage is the locality of the discretization, which makes them ideally suited for parallel computing. Moreover, DG methods incorporate non-homogeneous boundary conditions in the weak formulations, which greatly increases the robustness and accuracy of boundary condition implementation. In the past five years, Dr. Wang have worked with Professor Weimin Han (University of Iowa), Professor Xiaoliang Cheng (Zhejiang University) and other collaborators on developing DG methods for solving variational inequalities. Due to inequality form of the problems, the Galerkin orthogonality is lost when DG methods are applied to solve VIs, resulting in substantial difficulty in analyzing DG methods for VIs. Also, the bilinear forms of DG schemes have coercivity only in the finite element spaces, not in the original function space. Therefore, the standard analytical techniques of finite element methods for VIs are not applicable for DG cases. We had to search for a new approach. We studied numerous DG methods for solving elliptic variational inequalities of both the first and second kinds, and established a unified a priori error analysis. We proved that the consistent and stable DG schemes can achieve the optimal convergence order with linear element for various variational inequality problems, such as obstacle problem ([1]) , frictional contact problem ([1]), Signorini problem ([2]), and quasi-static contact problem ([3]).

[1] Fei Wang, Weimin Han and Xiaoliang Cheng, Discontinuous Galerkin Methods for Solving Elliptic Variational Inequalities, * SIAM J Numerical Analysis*, 48 (2010), 708-733.

[2] Fei Wang, Weimin Han and Xiaoliang Cheng, Discontinuous Galerkin Methods for Solving Signorini Problem, * IMA Journal of Numerical Analysis*, 31 (2011), 1754-1772.

[3] Fei Wang, Weimin Han and Xiaoliang Cheng, Discontinuous Galerkin Methods for Solving the Quasistatic Contact Problem, * Numerische Mathematik*, 126 (2014), 771-800.

Compared with uniform mesh, adaptive finite element method can improve precision with smaller memory and less time cost. DG methods are very suitable for hp-adaptive algorithm. Thus, it is a natural choice to study adaptive DG methods for VIs. For the DG schemes of obstacle problems, we derived reliable error estimators of the residual type. The efficiency of the estimators is theoretically explored and numerically confirmed ([4]). It is difficult to develop a posteriori error estimates to variational inequalities due to the inequality feature. The idea was to use Lagrange multiplier to transfer obstacle problem to the associated elliptic PDE, then apply a posteriori error theory of elliptic differential equation to derive reliable error estimators. This idea was extended to give a posteriori error analysis for VIs of the second kind. Moreover, a proof was provided for the efficiency of the error estimators, which was first proved in ([5]). A posteriori error estimates of DG methods for a frictional contact problem was studied, and we proved that the error estimators are reliable and efficient. The following two figures are for one numerical example about adaptive DG for solving obstacle problem. Figure 1 shows the adaptive meshes with hanging nodes, N denoting the number of elements. In Figure 2, the L^{2} norm and H^{1} norm errors are given respectively on L^{2} norm error are 0.097020 and 0.001293, respectively. To achieve the same level of accuracy, with adaptivity, we only need to compute the solution on the mesh $T_{11}$; correspondingly N = 3975, and H^{1} norm and L^{2} norm errors being 0.088184 and 0.001134, respectively. Therefore comparing to the uniform refinement, with smaller memory, the adaptive strategy saves lots of computing time.

Figure 1. Adaptive Refined Meshes Figure 2. Errors on Adaptive and Uniform Meshes

[4] Fei Wang, Weimin Han, Joseph Eichholz and Xiaoliang Cheng, A Posteriori Error Estimates of Discontinuous Galerkin Methods for Obstacle Problems, * Nonlinear Analysis: Real World Applications*, 22 (2015), 664-679.

[5] Fei Wang and Weimin Han, Another View for a Posteriori Error Estimates for Variational Inequalities of the Second Kind, * Applied Numerical Mathematics*, 72 (2013), 225-233.

To solve 4th order elliptic PDEs, conforming finite element (FE) method uses C^{1} finite elements, which are not easy to construct and implement. To resolve this problem, nonconforming FE method have been developed. However, the nonconforming FE space needs to be carefully chosen so that the inconsistent error can be controlled. Instead, we can consider C^{0} DG methods to solve 4th order PDEs. The FE spaces belong to C^{0}, instead of C^{1}, and at the meantime, penalty terms for derivative between element boundary are added to force the derivative almost continuous. Therefore, C^{0} DG schemes have good accuracy with less number of freedom, so a lot of time can be saved in solving the discretized problems. We study C^{0} DG methods to solve elliptic variational inequalities of 4th-order for the Kirchhoff plates. It is difficult to construct stable DG methods for such problems because of the higher order and the inequality form. For the frictional contact Kirchhoff plates, we proved that the quadratic C^{0} DG schemes achieve the optimal convergence order ([6]).

[6] Fei Wang, Tianyi Zhang and Weimin Han, C^{0} Discontinuous Galerkin Methods for a Plate Contact Problem, **Journal of Computational Mathematics**, accepted.

Interface Coupled Multi-Physics Problems

Many problems in physics and engineering contain a certain level of coupling between different physical systems, for example, fluid-structure Interaction (FSI) problems, and multi-phase flows, etc. FSI is a coupled partial differential equation system, which describes how a moving solid object interacts with the fluid surrounding it. This problem is very complex and challenging due to its highly nonlinear and coupled nature. Moreover, the position and the shape of the interface between fluid and structure is time dependent and is one of the unknowns, which greatly increases the nonlinearity of the problems both theoretically and computationally. FSI has a wide range of applications in many areas including hydro turbines and aircraft designing, artificial heart simulations and etc. Mathematically, such a problem contains an interface where the coupling takes place. Therefore, the numerical discretization of the interface problem is very important in the applied sciences and mathematics. The standard finite element cannot achieve the optimal convergence order due to lack of resolution for the interface. The interface-fitted mesh can be used to overcome this difficulty, but if the problem is time-dependent, the domain needs to be re-meshed at each time step, which introduces interpolation error between two meshes. The extended finite element method extends the classical finite element method by enriching the solution space locally around the interface with discontinuous functions, and it is realized through the partition of unity concept. This approach provides accurate approximation for the problems with jumps, singularities, and other locally nonsmooth features within elements. The hardest part of error analysis for XFEM is the trace and inverse inequalities on the curved sub-elements divided by interface from a triangle element, especially they do not hold true for the shape irregular sub-element. Therefore, the standard analysis for finite element methods cannot be applied to the analysis of stability for the high order XFEM. Worked with Dr. Shuo Zhang (Chinese Academy of Sciences), we applied quadratic XFEM with Nitsche scheme to solve elliptic interface problems. We have proved one key inequality for any shape of interface under certain assumptions, and then we are able to show that this method achieves the optimal convergence order ([7]). Furthermore, recently, I have been working with Prof. Xu and Dr. Yuanming Xiao (Nanjing University) on arbitrary order XFEM with Discontinuous Galerkin schemes on interface problem. Optimal error estimates in the piecewise H^{1}-norm and in the L^{2}-norm are rigorously proved for both schemes. In particular, we have devised a new parameter-friendly DG-XFEM method, which means that no “sufficiently large” parameters are needed to ensure the optimal convergence of the scheme ([8]). This parameter-friendly DG-XFEM method can be applied to solve interface couplied multiphysics problems, like the simulations of hydroelectric power generator, cardiovascular system and hydraulic fracturing.

[7] Fei Wang and Shuo Zhang, Optimal Quadratic Nitsche Extended Finite Element Method for Solving Interface Problem, **Journal of Computational Mathematics, **36 (2018), 693-717.

[8] Fei Wang, Yuanming Xiao and Jinchao Xu, High-Order Extended Finite Element Methods for Solving Interface Problems, submitted.

Unified Framework of Finite Element Methods

With Prof. Xu, Dr. Wu, Dr. Hong, we present a unified framework ([9]) for the design of finite element methods (FEM) of different kinds, including conforming and nonconforming FEMs, mixed FEMs, hybrid FEMs, discontinuous Galerkin (DG) methods, hybrid DG methods and weak Galerkin methods. We illustrate the main idea using a model second order elliptic boundary problem. This problem admits two main variational formulations, namely the primal and mixed formulation respectively. The primal formulation requires certain continuity for the primal variable u, namely u belongs to H^{1}$, while the mixed formulation requires certain continuity for the (mixed) flux variable p, namely p belongs to H(div). The design of finite element methods then amounts to an appropriate approximation of the aforementioned continuity requirements for either u or p. There are roughly four different ways to approximately impose these continuity requirements: (1) strongly (conforming); (2) weakly (nonconforming); (3) by Lagrangian multiplier (hybrid or stabilized hybrid), and (4) by penalization (discontinuous Galerkin). Using the notation of the DG-gradient and DG-divergence (which are reduced to standard weak derivatives when the underlying finite element spaces are conforming), every existing finite element method considered in this paper can be expressed in terms of one of the following: (1) DG-gradient and DG-gradient^{*} (primal), (2) DG-divergence and DG-divergence^{*} (mixed), and (3) DG-divergence^{*} and DG-gradient^{*} (DG).

[9] Qingguo Hong, Fei Wang, Shuonan Wu and Jinchao Xu*, A Unified Study of Continuous and Discontinuous Galerkin Methods, **SCIENCE CHINA Mathematics**, accepted.

Virtual Element Methods for VIs

The Finite element method (FEM) is a natural numerical discretization approach for variational inequalities. The classical FEM works on the elements with simple geometries, like triangles and rectangles. Due to the flexibility on constructing the local function space, the discontinuous Galerkin (DG) method can handle very general meshes with hanging nodes, which make them very suitable for hp-adaptivity. However, the DG method needs large number of degrees of freedom. Recently, borrowing the ideas from the mimetic finite difference method, the virtual element method (VEM), which can be regarded as a generalization of the classic FEM to general polygonal meshes, have been developed for solving a variety of partial differential equation. The virtual element method can handle very general (non-convex) polygonal elements with an arbitrary number of edges. In addition, it allows geometrical hanging nodes in the meshes, even they are treated as vertices of the polygonal elements in practice, so it is easy to implement h-adaptive strategy for the VEM. We study the virtual element method for solving obstacle problem [10], simplified friction problem [11], and plate frictional contact problem [12]. The optimal error estimates are established for the lowest-order virtual element methods, and numerical examples are given to support the theoretical results.

[10] Fei Wang and Huayi Wei, Virtual Element Methods for Obstacle Problem, **IMA Journal of Numerical Analysis,** accepted.

[11] Fei Wang* and Huayi Wei, Virtual Element Method for Simplified Friction Problem, **Applied Mathematics Letters**, (2018) https://doi.org/10.1016/j.aml.2018.06.002.

[12] Fei Wang and Jikun Zhao, Conforming and Nonconforming Virtual Element Methods for a Kirchhoff Plate Contact Problem, submitted.

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