样式： 排序： IF:  GO 导出 标记为已读

Shape Sensitivity Analysis in Aerodynamics Using an Isogeometric Discontinuous Galerkin Method SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210923
Maxime Stauffert, Régis DuvigneauSIAM Journal on Scientific Computing, Volume 43, Issue 5, Page B1081B1104, January 2021. The sensitivity equation method aims at estimating the derivative of the solution of partial differential equations with respect to a parameter of interest. The objective of this work is to investigate the ability of an isogeometric discontinuous Galerkin (DG) method to evaluate accurately sensitivities with respect

The AAAtrig Algorithm for Rational Approximation of Periodic Functions SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210923
Peter J. BaddooSIAM Journal on Scientific Computing, Volume 43, Issue 5, Page A3372A3392, January 2021. We present an extension of the AAA (adaptive AntoulasAnderson) algorithm for periodic functions, called “AAAtrig.” The algorithm uses the key steps of AAA approximation by (i) representing the approximant in (trigonometric) barycentric form and (ii) selecting the support points greedily. Accordingly, AAAtrig

Symmetrically Processed Splitting Integrators for Enhanced Hamiltonian Monte Carlo Sampling SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210923
S. Blanes, M. P. Calvo, F. Casas, J. M. SanzSernaSIAM Journal on Scientific Computing, Volume 43, Issue 5, Page A3357A3371, January 2021. We construct integrators to be used in Hamiltonian (or Hybrid) Monte Carlo sampling. The new integrators are easily implementable and, for a given computational budget, may deliver five times as many accepted proposals as standard leapfrog/Verlet without impairing in any way the quality of the samples. They are

Communication Lower Bounds of Bilinear Algorithms for Symmetric Tensor Contractions SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210923
Edgar Solomonik, James Demmel, Torsten HoeflerSIAM Journal on Scientific Computing, Volume 43, Issue 5, Page A3328A3356, January 2021. We introduce a new theoretical framework for deriving lower bounds on data movement in bilinear algorithms. Bilinear algorithms are a general representation of fast algorithms for bilinear functions, which include computation of matrix multiplication, convolution, and symmetric tensor contractions. A bilinear

An Efficient Dynamical LowRank Algorithm for the BoltzmannBGK Equation Close to the Compressible Viscous Flow Regime SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210921
Lukas Einkemmer, Jingwei Hu, Lexing YingSIAM Journal on Scientific Computing, Volume 43, Issue 5, Page B1057B1080, January 2021. It has recently been demonstrated that dynamical lowrank algorithms can provide robust and efficient approximations to a range of kinetic equations. This is true especially if the solution is close to some asymptotic limit where it is known that the solution is lowrank. A particularly interesting case is the

Highly Efficient and Energy Dissipative Schemes for the Time Fractional AllenCahn Equation SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210921
Dianming Hou, Chuanju XuSIAM Journal on Scientific Computing, Volume 43, Issue 5, Page A3305A3327, January 2021. In this paper, we propose and analyze a time stepping method for the time fractional AllenCahn equation. The key property of the proposed method is its unconditional stability for general meshes, including the graded mesh commonly used for this type of equation. The unconditional stability is proved through

A Variational Method for Generating $n$Cross Fields Using HigherOrder $Q$Tensors SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210921
Dmitry Golovaty, Jose Alberto Montero, Daniel SpirnSIAM Journal on Scientific Computing, Volume 43, Issue 5, Page A3269A3304, January 2021. An $n$cross field is a locally defined orthogonal coordinate system invariant with respect to the hyperoctahedral symmetry group (cubic for $n=3$). Cross fields are finding widespread use in mesh generation, computer graphics, and materials science among many applications. It was recently shown in [A. Chemin

Full Waveform Inversion Using Extended and Simultaneous Sources SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210920
Sagi Buchatsky, Eran TreisterSIAM Journal on Scientific Computing, Ahead of Print. PDEconstrained optimization problems are often treated using the reduced formulation where the PDE constraints are eliminated. This approach is known to be more computationally feasible than other alternatives at large scales. However, the elimination of the constraints forces the optimization process to fulfill the constraints at all times. In

Efficient Scaling and Moving Techniques for Spectral Methods in Unbounded Domains SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210920
Mingtao Xia, Sihong Shao, Tom ChouSIAM Journal on Scientific Computing, Volume 43, Issue 5, Page A3244A3268, January 2021. When using Laguerre and Hermite spectral methods to numerically solve PDEs in unbounded domains, the number of collocation points assigned inside the region of interest is often insufficient, particularly when the region is expanded or translated in order to safely capture the unknown solution. Simply increasing

The Random Feature Model for InputOutput Maps between Banach Spaces SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210920
Nicholas H. Nelsen, Andrew M. StuartSIAM Journal on Scientific Computing, Volume 43, Issue 5, Page A3212A3243, January 2021. Well known to the machine learning community, the random feature model is a parametric approximation to kernel interpolation or regression methods. It is typically used to approximate functions mapping a finitedimensional input space to the real line. In this paper, we instead propose a methodology for use of

An HMultigrid Method for Hybrid HighOrder Discretizations SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210916
Daniele A. Di Pietro, Frank Hülsemann, Pierre Matalon, Paul Mycek, Ulrich Rüde, Daniel RuizSIAM Journal on Scientific Computing, Ahead of Print. We consider a secondorder elliptic PDE discretized by the hybrid highorder method, for which globally coupled unknowns are located at faces. To efficiently solve the resulting linear system, we propose a geometric multigrid algorithm that keeps the degrees of freedom on the faces at every grid level. The core of the algorithm lies in the design

Combining Machine Learning and Adaptive Coarse SpacesA Hybrid Approach for Robust FETIDP Methods in Three Dimensions SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210916
Alexander Heinlein, Axel Klawonn, Martin Lanser, Janine WeberSIAM Journal on Scientific Computing, Ahead of Print. The hybrid MLFETIDP algorithm combines the advantages of adaptive coarse spaces in domain decomposition methods and certain supervised machine learning techniques. Adaptive coarse spaces ensure robustness of highly scalable domain decomposition solvers, even for highly heterogeneous coefficient distributions with arbitrary coefficient jumps. However

Search Direction Correction with Normalized Gradient Makes FirstOrder Methods Faster SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210916
Yifei Wang, Zeyu Jia, Zaiwen WenSIAM Journal on Scientific Computing, Volume 43, Issue 5, Page A3184A3211, January 2021. The socalled fast inertial relaxation engine is a firstorder method for unconstrained smooth optimization problems. It updates the search direction by a linear combination of the past search direction, the current gradient, and the normalized gradient direction. We explore more general combination rules and

A DiffusionDriven Characteristic Mapping Method for Particle Management SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210914
XiYuan Yin, Linan Chen, JeanChristophe NaveSIAM Journal on Scientific Computing, Volume 43, Issue 5, Page A3155A3183, January 2021. We present a novel particle management method using the characteristic mapping framework. In the context of explicit evolution of parametrized curves and surfaces, the surface distribution of marker points created from sampling the parametric space is controlled by the area element of the parametrization function

Deep Splitting Method for Parabolic PDEs SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210913
Christian Beck, Sebastian Becker, Patrick Cheridito, Arnulf Jentzen, Ariel NeufeldSIAM Journal on Scientific Computing, Volume 43, Issue 5, Page A3135A3154, January 2021. In this paper, we introduce a numerical method for nonlinear parabolic partial differential equations (PDEs) that combines operator splitting with deep learning. It divides the PDE approximation problem into a sequence of separate learning problems. Since the computational graph for each of the subproblems is

Covariance Models and Simulation Algorithm for Stationary Vector Random Fields on Spheres Crossed with Euclidean Spaces SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210913
Xavier Emery, Alfredo Alegría, Daisy ArroyoSIAM Journal on Scientific Computing, Volume 43, Issue 5, Page A3114A3134, January 2021. This paper focuses on vector random fields defined on $\mathbb{S}^d\times \mathbb{R}^k$, $d \geq 2$ and $k \geq 1$, with covariance functions that depend on the geodesic distance in $\mathbb{S}^d$ and on the separation vector in $\mathbb{R}^k$. First, we propose parametric families of nonseparable covariance functions

Fast Randomized NonHermitian Eigensolvers Based on Rational Filtering and Matrix Partitioning SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210913
Vassilis Kalantzis, Yuanzhe Xi, Lior HoreshSIAM Journal on Scientific Computing, Ahead of Print. This paper describes a set of rational filtering algorithms to compute a few eigenvalues (and associated eigenvectors) of nonHermitian matrix pencils. Our interest lies in computing eigenvalues located inside a given disk, and the proposed algorithms approximate these eigenvalues and associated eigenvectors by harmonic RayleighRitz projections

Robust and Effective eSIF Preconditioning for General Dense SPD Matrices SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210913
Jianlin XiaSIAM Journal on Scientific Computing, Ahead of Print. We propose an unconditionally robust and highly effective preconditioner for general dense symmetric positive definite matrices based on structured incomplete factorization (SIF), called the enhanced SIF (eSIF) preconditioner. The original SIF strategy proposed recently derives a structured preconditioner by applying block diagonal preprocessing

A Nonmonotone MatrixFree Algorithm for Nonlinear EqualityConstrained LeastSquares Problems SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210913
El Houcine Bergou, Youssef Diouane, Vyacheslav Kungurtsev, Clément W. RoyerSIAM Journal on Scientific Computing, Ahead of Print. Least squares form one of the most prominent classes of optimization problems with numerous applications in scientific computing and data fitting. When such formulations aim at modeling complex systems, the optimization process must account for nonlinear dynamics by incorporating constraints. In addition, these systems often incorporate a large

ImplicitExplicit Multirate Infinitesimal GARK Methods SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210909
Rujeko Chinomona, Daniel R. ReynoldsSIAM Journal on Scientific Computing, Volume 43, Issue 5, Page A3082A3113, January 2021. This work focuses on the development of a new class of highorder accurate methods for multirate time integration of systems of ordinary differential equations. Unlike other recent work in this area, the proposed methods support mixed implicitexplicit (IMEX) treatment of the slow time scale. In addition to allowing

Understanding and Mitigating Gradient Flow Pathologies in PhysicsInformed Neural Networks SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210909
Sifan Wang, Yujun Teng, Paris PerdikarisSIAM Journal on Scientific Computing, Volume 43, Issue 5, Page A3055A3081, January 2021. The widespread use of neural networks across different scientific domains often involves constraining them to satisfy certain symmetries, conservation laws, or other domain knowledge. Such constraints are often imposed as soft penalties during model training and effectively act as domainspecific regularizers

Algorithms for the Rational Approximation of MatrixValued Functions SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210909
Ion Victor Gosea, Stefan GüttelSIAM Journal on Scientific Computing, Volume 43, Issue 5, Page A3033A3054, January 2021. A selection of algorithms for the rational approximation of matrixvalued functions are discussed, including variants of the interpolatory adaptive AntoulasAnderson (AAA) method, the rational Krylov fitting (RKFIT) method based on approximate least squares fitting, vector fitting, and a method based on lowrank

A Posteriori Error Estimates for Multilevel Methods for Graph Laplacians SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210907
Xiaozhe Hu, Kaiyi Wu, Ludmil T. ZikatanovSIAM Journal on Scientific Computing, Ahead of Print. In this paper, we study a posteriori error estimators which aid multilevel iterative solvers for linear systems of graph Laplacians. In earlier works such estimates were computed by solving a perturbed global optimization problem, which could be computationally expensive. We propose a novel strategy to compute these estimates by constructing a Helmholtz

PNKHB: A Projected NewtonKrylov Method for LargeScale BoundConstrained Optimization SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210907
Kelvin Kan, Samy Wu Fung, Lars RuthottoSIAM Journal on Scientific Computing, Ahead of Print. We present PNKHB, a projected NewtonKrylov method for iteratively solving largescale optimization problems with bound constraints. PNKHB is geared toward situations in which function and gradient evaluations are expensive, and the (approximate) Hessian is only available through matrixvector products. This is commonly the case in largescale

AMG Preconditioners for Linear Solvers towards Extreme Scale SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210826
Pasqua D'Ambra, Fabio Durastante, Salvatore FilipponeSIAM Journal on Scientific Computing, Ahead of Print. Linear solvers for large and sparse systems are a key element of scientific applications, and their efficient implementation is necessary to harness the computational power of current computers. Algebraic MultiGrid (AMG) preconditioners are a popular ingredient of such linear solvers; this is the motivation for the present work, where we examine

A New TroubledCell Indicator for Discontinuous Galerkin Methods Using KMeans Clustering SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210826
Hongqiang Zhu, Haiyun Wang, Zhen GaoSIAM Journal on Scientific Computing, Volume 43, Issue 4, Page A3009A3031, January 2021. An accurate troubledcell indicator (TCI) is always desired by the discontinuous Galerkin methods for solving nonlinear hyperbolic conservation laws. In this paper, we will design a new TCI using Kmeans clustering. The values of a troubledcell indication variable are in general of different magnitudes between

Positivity Preservation of a FirstOrder Scheme for a QuasiConservative Compressible TwoMaterial Model SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210824
Khosro ShahbaziSIAM Journal on Scientific Computing, Volume 43, Issue 4, Page B1029B1055, January 2021. The positivity preservation or hyperbolicity preservation of numerical schemes is essential for robust computations of compressible twofluid models. Due to the quasiconservative (nonconservative) nature of the compressible twofluid model and use of the stiffenedgas equation of state that allows liquidgas

Recovery of a TimeDependent Bottom Topography Function from the Shallow Water Equations via an Adjoint Approach SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210824
Jolene Britton, Yat Tin Chow, Weitao Chen, Yulong XingSIAM Journal on Scientific Computing, Volume 43, Issue 4, Page A2981A3008, January 2021. We develop an adjoint approach for recovering the topographical function included in the source term of onedimensional hyperbolic balance laws. We focus on a specific system, namely, the shallow water equations, in an effort to recover the riverbed topography. The novelty of this work is the ability to robustly

Iteratively Reweighted Group Lasso Based on LogComposite Regularization SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210820
Chengyu Ke, Miju Ahn, Sunyoung Shin, Yifei LouSIAM Journal on Scientific Computing, Ahead of Print. The paper considers supervised learning problems of labeled data with grouped input features. The groups are nonoverlapped such that the model coefficients corresponding to the input features form disjoint groups. The coefficients have group sparsity structure in the sense that coefficients corresponding to each group shall be simultaneously either

A LowRank Matrix Equation Method for Solving PDEConstrained Optimization Problems SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210819
Alexandra Bünger, Valeria Simoncini, Martin StollSIAM Journal on Scientific Computing, Ahead of Print. PDEconstrained optimization problems arise in a broad number of applications such as hyperthermia cancer treatment and blood flow simulation. Discretization of the optimization problem and using a Lagrangian approach result in a largescale saddlepoint system, which is challenging to solve, and acquiring a full spacetime solution is often infeasible

Local Fourier Analysis of Multigrid for Hybridized and Embedded Discontinuous Galerkin Methods SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210819
Yunhui He, Sander Rhebergen, Hans De SterckSIAM Journal on Scientific Computing, Ahead of Print. In this paper we present a geometric multigrid method with Jacobi and Vanka relaxation for hybridized and embedded discontinuous Galerkin discretizations of the Laplacian. We present a local Fourier analysis (LFA) of the twogrid errorpropagation operator and show that the multigrid method applied to an embedded discontinuous Galerkin (EDG) discretization

FrequencyStable Full Maxwell in Electroquasistatic Gauge SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210819
Jörg Ostrowski, Ralf HiptmairSIAM Journal on Scientific Computing, Volume 43, Issue 4, Page B1008B1028, January 2021. The electroquasistatic approximation of Maxwell equations is commonly used to model coupled resistive/capacitive phenomena at low frequencies. It neglects induction and becomes unstable in the stationary limit. We introduce a stabilization that prevents this lowfrequency breakdown. It results in a system for

Adaptive Hamiltonian Variational Integrators and Applications to Symplectic Accelerated Optimization SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210819
Valentin Duruisseaux, Jeremy Schmitt, Melvin LeokSIAM Journal on Scientific Computing, Volume 43, Issue 4, Page A2949A2980, January 2021. It is well known that symplectic integrators lose their near energy preservation properties when variable timesteps are used. The most common approach to combining adaptive timesteps and symplectic integrators involves the Poincaré transformation of the original Hamiltonian. In this article, we provide a framework

Numerical Modeling of the FluidPorohyperelastic Structure Interaction SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210819
Anyastassia Seboldt, Oyekola Oyekole, Josip Tambača, Martina BukačSIAM Journal on Scientific Computing, Volume 43, Issue 4, Page A2923A2948, January 2021. We consider a moving domain, fluidporohyperelastic structure interaction problem in a dualmixed formulation. The fluid is described using the NavierStokes equations, and the porohyperelastic structure is described using the Biot equations. To solve this problem numerically, we propose two novel, partitioned

Approximation of Integral Fractional Laplacian and Fractional PDEs via sincBasis SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210810
Harbir Antil, Patrick Dondl, Ludwig StrietSIAM Journal on Scientific Computing, Volume 43, Issue 4, Page A2897A2922, January 2021. Fueled by many applications in random processes, imaging science, geophysics, etc., fractional Laplacians have recently received significant attention. The key driving force behind the success of this operator is its ability to capture nonlocal effects while enforcing less smoothness on functions. In this article

Stabilized Scalar Auxiliary Variable Ensemble Algorithms for Parameterized Flow Problems SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210810
Nan Jiang, Huanhuan YangSIAM Journal on Scientific Computing, Volume 43, Issue 4, Page A2869A2896, January 2021. Computing a flow system a number of times with different samples of flow parameters is a common practice in many uncertainty quantification applications, which can be prohibitively expensive for complex nonlinear flow problems. This report presents two second order, stabilized, scalar auxiliary variable (SAV)

Multilevel Quasi Monte Carlo Methods for Elliptic PDEs with Random Field Coefficients via Fast White Noise Sampling SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210809
Matteo Croci, Michael Giles, Patrick E. FarrellSIAM Journal on Scientific Computing, Volume 43, Issue 4, Page A2840A2868, January 2021. When solving partial differential equations (PDEs) with random fields as coefficients, the efficient sampling of random field realizations can be challenging. In this paper we focus on the fast sampling of Gaussian fields using quasirandom points in a finite element and multilevel quasi Monte Carlo (MLQMC) setting

Scalable Algorithms for Multiple Network Alignment SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210805
Huda Nassar, Georgios Kollias, Ananth Grama, David F. GleichSIAM Journal on Scientific Computing, Ahead of Print. Multiple network alignment is the problem of identifying similar and related regions in a given set of networks. While there are a large number of effective techniques for pairwise problems with two networks that scale in terms of edges, these cannot be readily extended to align multiple networks as the computational complexity will tend to grow

A ThreeOperator Splitting Algorithm for Nonconvex Sparsity Regularization SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210805
Fengmiao Bian, Xiaoqun ZhangSIAM Journal on Scientific Computing, Volume 43, Issue 4, Page A2809A2839, January 2021. Sparsity regularization has been widely adopted in many fields, such as signal and image processing and machine learning. In this paper, we mainly consider nonconvex minimization problems involving three terms, for applications such as sparse signal recovery and low rank matrix recovery. We employ a threeoperator

GPUAccelerated Discontinuous Galerkin Methods on Polytopic Meshes SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210804
Zhaonan Dong, Emmanuil H. Georgoulis, Thomas KappasSIAM Journal on Scientific Computing, Volume 43, Issue 4, Page C312C334, January 2021. Discontinuous Galerkin (dG) methods on meshes consisting of polygonal/polyhedral (henceforth, collectively termed as polytopic) elements have received considerable attention in recent years. Due to the physical frame basis functions used typically and the quadrature challenges involved, the matrixassembly step

Comparison of Accuracy and Scalability of GaussNewton and Alternating Least Squares for CANDECOMC/PARAFAC Decomposition SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210804
Navjot Singh, Linjian Ma, Hongru Yang, Edgar SolomonikSIAM Journal on Scientific Computing, Volume 43, Issue 4, Page C290C311, January 2021. Alternating least squares is the most widely used algorithm for CANDECOMC/PARAFAC (CP) tensor decomposition. However, alternating least squares may exhibit slow or no convergence, especially when high accuracy is required. An alternative approach is to regard CP decomposition as a nonlinear least squares problem

Parameter Robust Preconditioning by Congruence for MultipleNetwork Poroelasticity SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210804
E. Piersanti, J. J. Lee, T. Thompson, K.A. Mardal, M. E. RognesSIAM Journal on Scientific Computing, Volume 43, Issue 4, Page B984B1007, January 2021. The mechanical behavior of a poroelastic medium permeated by multiple interacting fluid networks can be described by a system of timedependent partial differential equations known as the multiplenetwork poroelasticity (MPET) equations or multiporosity/multipermeability systems. These equations generalize Biot's

Robust Preconditioners for Perturbed SaddlePoint Problems and Conservative Discretizations of Biot's Equations Utilizing Total Pressure SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210804
Wietse M. Boon, Miroslav Kuchta, KentAndre Mardal, Ricardo RuizBaierSIAM Journal on Scientific Computing, Volume 43, Issue 4, Page B961B983, January 2021. We develop robust solvers for a class of perturbed saddlepoint problems arising in the study of a secondorder elliptic equation in mixed form (in terms of flux and potential), and of the fourfield formulation of Biot's consolidation problem for linear poroelasticity (using displacement, filtration flux, total

A Posteriori Error Estimates for Adaptive QM/MM Coupling Methods SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210804
Yangshuai Wang, Huajie Chen, Mingjie Liao, Christoph Ortner, Hao Wang, Lei ZhangSIAM Journal on Scientific Computing, Volume 43, Issue 4, Page A2785A2808, January 2021. Hybrid quantum/molecular mechanics models (QM/MM methods) are widely used in material and molecular simulations when MM models do not provide sufficient accuracy but pure QM models are computationally prohibitive. Adaptive QM/MM coupling methods feature onthefly classification of atoms during the simulation

An AllatOnce Preconditioner for Evolutionary Partial Differential Equations SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210804
Xuelei Lin, Michael NgSIAM Journal on Scientific Computing, Volume 43, Issue 4, Page A2766A2784, January 2021. In [McDonald, Pestana, and Wathen, SIAM J. Sci. Comput., 40 (2018), pp. A1012A1033], a block circulant preconditioner is proposed for allatonce linear systems arising from evolutionary partial differential equations, in which the preconditioned matrix is proven to be diagonalizable and to have identitypluslowrank

Multilevel Spectral Coarsening for Graph Laplacian Problems with Application to Reservoir Simulation SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210804
Andrew T. Barker, Stephan V. Gelever, Chak S. Lee, Sarah V. Osborn, Panayot S. VassilevskiSIAM Journal on Scientific Computing, Volume 43, Issue 4, Page A2737A2765, January 2021. We extend previously developed twolevel coarsening procedures for graph Laplacian problems written in a mixed saddle point form to the fully recursive multilevel case. The resulting hierarchy of discretizations gives rise to a hierarchy of upscaled models, in the sense that they provide approximation in the natural

Efficient Direct SpaceTime Finite Element Solvers for Parabolic InitialBoundary Value Problems in Anisotropic Sobolev Spaces SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210804
Ulrich Langer, Marco ZankSIAM Journal on Scientific Computing, Volume 43, Issue 4, Page A2714A2736, January 2021. We consider a spacetime variational formulation of parabolic initialboundary value problems in anisotropic Sobolev spaces in combination with a Hilberttype transformation. This variational setting is the starting point for the spacetime Galerkin finite element discretization that leads to a large global linear

A Random Batch Ewald Method for Particle Systems with Coulomb Interactions SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210729
Shi Jin, Lei Li, Zhenli Xu, Yue ZhaoSIAM Journal on Scientific Computing, Volume 43, Issue 4, Page B937B960, January 2021. We develop a random batch Ewald (RBE) method for molecular dynamics simulations of particle systems with longrange Coulomb interactions, which achieves an $O(N)$ complexity in each step of simulating $N$body systems. The RBE method is based on the Ewald splitting for the Coulomb kernel with a random “minibatch”

A StructureExploiting Nested LanczosType Iteration for the Multiview Canonical Correlation Analysis SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210729
LeiHong Zhang, Xijun Ma, Chungen ShenSIAM Journal on Scientific Computing, Volume 43, Issue 4, Page A2685A2713, January 2021. Data points from many recent real applications usually have a multiview structure in the sense that they are drawn from a multivariate random variable $\mathtt{v}\in \mathbb{R}^n$ that can be partitioned into multiple, say, $m$, subvariables (i.e., multiview) $\mathtt{v}_i\in \mathbb{R}^{n_i}$ for $i=1,\ldots

Rational Spectral Filters with Optimal Convergence Rate SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210729
Konrad Kollnig, Paolo Bientinesi, Edoardo A. Di NapoliSIAM Journal on Scientific Computing, Volume 43, Issue 4, Page A2660A2684, January 2021. In recent years, contourbased eigensolvers have emerged as a standard approach for the solution of large and sparse eigenvalue problems. Building upon recent performance improvements through nonlinear leastsquares optimization of socalled rational filters, we introduce a systematic method to design these filters

A FiniteElement Framework for a Mimetic FiniteDifference Discretization of Maxwell's Equations SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210728
James H. Adler, Casey Cavanaugh, Xiaozhe Hu, Ludmil T. ZikatanovSIAM Journal on Scientific Computing, Volume 43, Issue 4, Page A2638A2659, January 2021. Maxwell's equations are a system of partial differential equations that govern the laws of electromagnetic induction. We study a mimetic finitedifference (MFD) discretization of the equations which preserves important underlying physical properties. We show that, after masslumping and appropriate scaling, the

Synchronous and Concurrent Multidomain Computing Method for Cloud Computing Platforms SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210720
Marcelino Anguiano, Paul Kuberry, Pavel Bochev, Arif MasudSIAM Journal on Scientific Computing, Ahead of Print. We present a numerical method for synchronous and concurrent solution of transient elastodynamics problem where the computational domain is divided into subdomains that may reside on separate computational platforms. This work employs the variational multiscale discontinuous Galerkin (VMDG) method to develop interdomain transmission conditions for

A StabilizerFree, PressureRobust, and Superconvergence Weak Galerkin Finite Element Method for the Stokes Equations on Polytopal Mesh SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210720
Lin Mu, Xiu Ye, Shangyou ZhangSIAM Journal on Scientific Computing, Volume 43, Issue 4, Page A2614A2637, January 2021. In this paper, we propose a new stabilizerfree and pressurerobust weak Galerkin finite element method for the Stokes equations with superconvergence on polytopal mesh in the primary velocitypressure formulation. Convergence rates with one order higher than the optimal order for velocity in both the energy norm

A New Class of AMG Interpolation Methods Based on MatrixMatrix Multiplications SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210719
Ruipeng Li, Björn Sjögreen, Ulrike Meier YangSIAM Journal on Scientific Computing, Ahead of Print. A new class of distancetwo interpolation methods for algebraic multigrid (AMG) that can be formulated in terms of sparse matrixmatrix multiplications is presented and analyzed. Compared with similar distancetwo prolongation operators [H. De Sterck et al., Numer. Linear Algebra Appl., 15 (2008), pp. 115139], the proposed algorithms exhibit improved

Extensions of the Augmented Block Cimmino Method to the Solution of Full Rank Rectangular Systems SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210719
Andrei Dumitrasc, Philippe Leleux, Constantin Popa, Ulrich Ruede, Daniel RuizSIAM Journal on Scientific Computing, Ahead of Print. For the solution of large sparse unsymmetric systems, Duff et al. [SIAM J. Sci. Comput., 37 (2015), pp. A1248A1269] proposed an approach based on the block Cimmino iterations [Numer. Math., 35 (1980), pp. 112], in which the solution is computed in a single iteration, so we call it a pseudodirect solver. In this approach, matrices are augmented

On the Convergence Rate of Variants of the Conjugate Gradient Algorithm in Finite Precision Arithmetic SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210715
Anne Greenbaum, Hexuan Liu, Tyler ChenSIAM Journal on Scientific Computing, Ahead of Print. We consider three mathematically equivalent variants of the conjugate gradient (CG) algorithm and how they perform in finite precision arithmetic. It was shown in [Greenbaum, Lin. Alg. Appl., 113 (1989), pp. 763] that under certain conditions involving local orthogonality and approximate satisfaction of a recurrence formula, that may be satisfied

Efficient Multiscale Algorithms for Simulating Nonlocal Optical Response of Metallic Nanostructure Arrays SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210715
Yongwei Zhang, Chupeng Ma, Liqun Cao, Dongyang ShiSIAM Journal on Scientific Computing, Volume 43, Issue 4, Page B907B936, January 2021. In this paper, we consider numerical simulations of the nonlocal optical response of metallic nanostructure arrays inside a dielectric host, which is of particular interest to the nanoplasmonics community due to many unusual properties and potential applications. Mathematically, it is described by Maxwell's equations

A Tailored Convolutional Neural Network for Nonlinear Manifold Learning of Computational Physics Data Using Unstructured Spatial Discretizations SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210715
John Tencer, Kevin PotterSIAM Journal on Scientific Computing, Volume 43, Issue 4, Page A2581A2613, January 2021. We propose a nonlinear manifold learning technique based on deep convolutional autoencoders that is appropriate for model order reduction of physical systems in complex geometries. Convolutional neural networks have proven to be highly advantageous for compressing data arising from systems demonstrating a slowdecaying

ScoreBased Parameter Estimation for a Class of ContinuousTime State Space Models SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210715
Alexandros Beskos, Dan Crisan, Ajay Jasra, Nikolas Kantas, Hamza RuzayqatSIAM Journal on Scientific Computing, Volume 43, Issue 4, Page A2555A2580, January 2021. We consider the problem of parameter estimation for a class of continuoustime state space models (SSMs). In particular, we explore the case of a partially observed diffusion, with data also arriving according to a diffusion process. Based upon a standard identity of the score function, we consider two particle

Approximate Error Bounds on Solutions of Nonlinearly Preconditioned PDEs SIAM J. Sci. Comput. (IF 2.373) Pub Date : 20210715
Lulu Liu, David E. KeyesSIAM Journal on Scientific Computing, Volume 43, Issue 4, Page A2526A2554, January 2021. In many multiphysics applications, an ultimate quantity of interest can be written as a linear functional of the solution to the discretized governing nonlinear partial differential equations and finding a sufficiently accurate pointwise solution may be regarded as a step toward that end. In this paper, we derive