Mathematics

In density-based topology optimization, design variables associated to the boundaries of the design domain require unique treatment to negate boundary effects arising from the filtering technique. An effective approach to deal with filtering boundary conditions is to extend the design domain beyond the borders using void densities. Although the approach is easy to implement, it introduces extra computational cost required to store the additional Finite Elements (FEs). This work proposes a numerical technique for the density filter that emulates an extension of the design domain, thus, it avoids boundary effects without demanding additional computational cost, besides it is very simple to implement on both regular and irregular meshes. The numerical scheme is demonstrated using the compliance minimization problem on two-dimensional design domains. In addition, this article presents a discussion regarding the use of a real extension of the design domain, where the Finite Element Analysis (FEA) and volume restriction are influenced. Through a quantitative study, it is shown that affecting FEA or volume restriction with an extension of the FE mesh promotes the disconnection of material from the boundaries of the design domain, which is interpreted as a numerical instability promoting convergence towards local optimums.

A new class of implicit-explicit (IMEX) methods combined with a p-adaptive mixed finite element formulation is proposed to simulate the diffusion of reacting species. Hierarchical polynomial functions are used to construct an H(Div) -conforming base for the flux vectors, and a non-conforming L 2 base for the mass concentration of the species. The mixed formulation captures the distinct nonlinearities associated with the constitutive flux equations and the reaction terms. The IMEX method conveniently treats these two sources of nonlinearity implicitly and explicitly, respectively, within a single time-stepping framework. The combination of the p-adaptive mixed formulation and the IMEX method delivers a robust and efficient algorithm. The proposed methods eliminate the coupled effect of mesh size and time step on the algorithmic stability. A residual based a posteriori error estimate that provides an upper bound of the natural error norm is derived. The availability of such estimate which can be obtained with minimal computational effort and the hierarchical construction of the finite element spaces allow for the formulation of an efficient p-adaptive algorithm. A series of numerical examples demonstrate the performance of the approach. It is shown that the method with the p-adaptive strategy accurately solves problems involving travelling waves, and those with discontinuities and singularities. The flexibility of the formulation is also illustrated via selected applications in pattern formation and electrophysiology.

In this paper, we study a parallel-in-time (PinT) algorithm for all-at-once system from a non-local evolutionary equation with weakly singular kernel where the temporal term involves a non-local convolution with a weakly singular kernel and the spatial term is the usual Laplacian operator with variable coefficients. We propose to use a two-sided preconditioning technique for the all-at-once discretization of the equation. Our preconditioner is constructed by replacing the variable diffusion coefficients with a constant coefficient to obtain a constant-coefficient all-at-once matrix. We split a square root of the constant Laplacian operator out of the constant-coefficient all-at-once matrix as a right preconditioner and take the remaining part as a left preconditioner, which constitutes our two-sided preconditioning. Exploiting the diagonalizability of the constant-Laplacian matrix and the triangular Toeplitz structure of the temporal discretization matrix, we obtain efficient representations of inverses of the right and the left preconditioners, because of which the iterative solution can be fast updated in a PinT manner. Theoretically, the condition number of the two-sided preconditioned matrix is proven to be uniformly bounded by a constant independent of the matrix size. To the best of our knowledge, for the non-local evolutionary equation with variable coefficients, this is the first attempt to develop a PinT preconditioning technique that has fast and exact implementation and that the corresponding preconditioned system has a uniformly bounded condition number. Numerical results are reported to confirm the efficiency of the proposed two-sided preconditioning technique.

A fully discrete finite element method, based on a new weak formulation and a new time-stepping scheme, is proposed for the surface diffusion flow of closed curves in the two-dimensional plane. It is proved that the proposed method can preserve two geometric structures simultaneously at the discrete level, i.e., the perimeter of the curve decreases in time while the area enclosed by the curve is conserved. Numerical examples are provided to demonstrate the convergence of the proposed method and the effectiveness of the method in preserving the two geometric structures.

In this paper, we develop a first order (in time) numerical scheme for the binary fluid surfactant phase field model. The free energy contains a double-well potential, a nonlinear coupling entropy and a Flory-Huggins potential. The resulting coupled system consists of two Cahn-Hilliard type equations. This system is solved numerically by finite difference spatial approximation, in combination with convex splitting temporal discretization. We prove the proposed scheme is unique solvable, positivity-preserving and unconditionally energy stable. In addition, an optimal rate convergence analysis is provided for the proposed numerical scheme, which will be the first such result for the binary fluid-surfactant system. Newton iteration is used to solve the discrete system. Some numerical experiments are performed to validate the accuracy and energy stability of the proposed scheme.

In this paper, we derive a posteriori error estimates for mixed-dimensional elliptic equations exhibiting a hierarchical structure. Exploiting the exterior calculus perspective of such equations, we introduce mixed-dimensional variables and operators, which, together with careful construction of the functional spaces, allow us to recast the set of partial differential equations as a regular linear elliptic problem structure-wise. We therefrom apply the well-established theory of functional a posteriori error estimates to our model to derive guaranteed abstract as well as fully computable upper bounds. Our estimators are tested using three different families of locally-mass conservative methods on synthetic problems and verification benchmarks of flow in fractured porous media. The numerical results support our theoretical findings while showcasing satisfactory effectivity indices.

In this paper, we develop a fast numerical method for solving the time-dependent Riesz space fractional diffusion equations with a nonlinear source term in the convex domain. An implicit finite difference method is employed to discretize the Riesz space fractional diffusion equations with a penalty term in a rectangular region by the volume-penalization approach. The stability and the convergence of the proposed method are studied. As the coefficient matrix is with the Toeplitz-like structure, the generalized minimum residual method with a preconditioner based on the sine transform is exploited to solve the discretized linear system, where the preconditioner is constructed in view of the combination of two approximate inverse ? matrices, which can be diagonalized by the sine transform. The spectrum of the preconditioned matrix is also investigated. Numerical experiments are carried out to demonstrate the efficiency of the proposed method.

This article on nonconforming schemes for m harmonic problems simultaneously treats the Crouzeix-Raviart ( m=1 ) and the Morley finite elements ( m=2 ) for the original and for modified right-hand side F in the dual space V ??:= H ?�m (Ω) to the energy space V:= H m 0 (Ω) . The smoother J: V nc ?�V in this paper is a companion operator, that is a linear and bounded right-inverse to the nonconforming interpolation operator I nc :V??V nc , and modifies the discrete right-hand side F h :=F?�J??V ??nc . The best-approximation property of the modified scheme from Veeser et al. (2018) is recovered and complemented with an analysis of the convergence rates in weaker Sobolev norms. Examples with oscillating data show that the original method may fail to enjoy the best-approximation property but can also be better than the modified scheme. The a~posteriori analysis of this paper concerns data oscillations of various types in a class of right-hand sides F??V ??. The reliable error estimates involve explicit constants and can be recommended for explicit error control of the piecewise energy norm. The efficiency follows solely up to data oscillations and examples illustrate this can be problematic.

The nonconforming virtual element method (NCVEM) for the approximation of the weak solution to a general linear second-order non-selfadjoint indefinite elliptic PDE in a polygonal domain is analyzed under reduced elliptic regularity. The main tool in the a priori error analysis is the connection between the nonconforming virtual element space and the Sobolev space H 1 0 (Ω) by a right-inverse J of the interpolation operator I h . The stability of the discrete solution allows for the proof of existence of a unique discrete solution, of a discrete inf-sup estimate and, consequently, for optimal error estimates in the H 1 and L 2 norms. The explicit residual-based a posteriori error estimate for the NCVEM is reliable and efficient up to the stabilization and oscillation terms. Numerical experiments on different types of polygonal meshes illustrate the robustness of an error estimator and support the improved convergence rate of an adaptive mesh-refinement in comparison to the uniform mesh-refinement.

Gradient descent optimization algorithms are the standard ingredients that are used to train artificial neural networks (ANNs). Even though a huge number of numerical simulations indicate that gradient descent optimization methods do indeed convergence in the training of ANNs, until today there is no rigorous theoretical analysis which proves (or disproves) this conjecture. In particular, even in the case of the most basic variant of gradient descent optimization algorithms, the plain vanilla gradient descent method, it remains an open problem to prove or disprove the conjecture that gradient descent converges in the training of ANNs. In this article we solve this problem in the special situation where the target function under consideration is a constant function. More specifically, in the case of constant target functions we prove in the training of rectified fully-connected feedforward ANNs with one-hidden layer that the risk function of the gradient descent method does indeed converge to zero. Our mathematical analysis strongly exploits the property that the rectifier function is the activation function used in the considered ANNs. A key contribution of this work is to explicitly specify a Lyapunov function for the gradient flow system of the ANN parameters. This Lyapunov function is the central tool in our convergence proof of the gradient descent method.