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1. Comparison of variational discretizations for a convection-diffusion problem

   Bacuta, Constantin; Bacuta, Cristina; Hayes, Daniel (December 2024, Revue Roumaine Mathématiques Pures et Appliquées)

   Beznea, Lucian; Putinar, Mihai (Ed.)

   For a model convection-diffusion problem, we obtain new error estimates for a general upwinding finite element discretization based on bubble modification of the test space. The key analysis tool is finding representations of the optimal norms on the trial spaces at the continuous and discrete levels. We analyze and compare three methods: the standard linear discretization, the saddle point least square and the upwinding Petrov-Galerkin methods. We conclude that the bubble upwinding Petrov-Galerkin method is the most performant discretization for the one dimensional model. Our results for the model convection-diffusion problem can be extended for creating new and efficient discretizations for the multidimensional cases. 

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2. Saddle point least squares discretization for convection-diffusion

   https://doi.org/10.1080/00036811.2023.2291511  

   Bacuta, Constantin; Hayes, Daniel; O'Grady, Tyler (August 2024, Applicable Analysis)

   We consider a model convection-diffusion problem and present our recent analysis and numerical results regarding mixed finite element formulation and discretization in the singular perturbed case when the convection term dominates the problem. Using the concepts of optimal norm and saddle point reformulation, we found new error estimates for the case of uniform meshes. We compare the standard linear Galerkin discretization to a saddle point least square discretization that uses quadratic test functions, and explain the non-physical oscillations of the discrete solutions. We also relate a known upwinding Petrov–Galerkin method and the stream-line diffusion discretization method, by emphasizing the resulting linear systems and by comparing appropriate error norms. The results can be extended to the multidimensional case in order to find efficient approximations for more general singular perturbed problems including convection dominated models. 

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3. Connections between finite difference and finite element approximations

   https://doi.org/https://doi.org/10.1080/00036811.2021.2005785  

   Constantin Bacuta; Cristina Bacuta (October 2021, Applicable analysis)

   Taylor And Francis Online (Ed.)

   We present useful connections between the finite difference and the finite element methods for a model boundary value problem. We start from the observation that, in the finite element context, the interpolant of the solution in one dimension coincides with the finite element approximation of the solution. This result can be viewed as an extension of the Green function formula for the solution at the continuous level. We write the finite difference and the finite element systems such that the two corresponding linear systems have the same stiffness matrices and compare the right hand side load vectors for the two methods. Using evaluation of the Green function, a formula for the inverse of the stiffness matrix is extended to the case of non-uniformly distributed mesh points. We provide an error analysis based on the connection between the two methods and estimate the energy norm of the difference of the two solutions. Interesting extensions to the 2D case are provided. 

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4. Assessment of numerical schemes for transient, finite-element ice flow models using ISSM v4.18

   https://doi.org/10.5194/gmd-14-2545-2021  

   dos Santos, Thiago Dias; Morlighem, Mathieu; Seroussi, Hélène (January 2021, Geoscientific Model Development)

   Abstract. Time-dependent simulations of ice sheets require two equations to be solved:the mass transport equation, derived from the conservation of mass, and thestress balance equation, derived from the conservation of momentum. The masstransport equation controls the advection of ice from the interior of the icesheet towards its periphery, thereby changing its geometry. Because it isbased on an advection equation, a stabilization scheme needs to beemployed when solved using the finite-element method. Several stabilizationschemes exist in the finite-element method framework, but their respectiveaccuracy and robustness have not yet been systematically assessed forglaciological applications. Here, we compare classical schemes used in thecontext of the finite-element method: (i) artificial diffusion, (ii)streamline upwinding, (iii) streamline upwind Petrov–Galerkin, (iv)discontinuous Galerkin, and (v) flux-corrected transport. We also look at thestress balance equation, which is responsible for computing the ice velocitythat “advects” the ice downstream. To improve the velocity computationaccuracy, the ice-sheet modeling community employs several sub-elementparameterizations of physical processes at the grounding line, the point wherethe grounded ice starts to float onto the ocean. Here, we introduce a newsub-element parameterization for the driving stress, the force that drives theice-sheet flow. We analyze the response of each stabilization scheme byrunning transient simulations forced by ice-shelf basal melt. The simulationsare based on an idealized ice-sheet geometry for which there is no influenceof bedrock topography. We also perform transient simulations of the AmundsenSea Embayment, West Antarctica, where real bedrock and surface elevations areemployed. In both idealized and real ice-sheet experiments, stabilizationschemes based on artificial diffusion lead systematically to a bias towardsmore mass loss in comparison to the other schemes and therefore should beavoided or employed with a sufficiently high mesh resolution in the vicinityof the grounding line. We also run diagnostic simulations to assess theaccuracy of the driving stress parameterization, which, in combination with anadequate parameterization for basal stress, provides improved numericalconvergence in ice speed computations and more accurate results. 

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5. Scalable DPG multigrid solver with applications in high-frequency wave propagation

   https://doi.org/10.26153/tsw/55685  

   Badger, Jacob (January 2024, The University of Texas at Austin)

   Wave propagation is fundamental to applications including natural resource exploration, nuclear fusion research, and military defense, among others. However, developing accurate and efficient numerical algorithms for solving time-harmonic wave propagation problems is notoriously difficult. One difficulty is that classical discretization techniques (e.g., Galerkin finite elements, finite difference, etc.) yield indefinite discrete systems that preclude the use of many scalable solution algorithms. Significant progress has been made to develop specialized preconditioners for high-frequency wave propagation problems but robust and scalable solvers for general problems, including non-homogenous media and complex geometries, remain elusive. An alternative approach is to use minimum residual discretization methods—that yield Hermitian positive-definite discrete systems—and may be amenable to more standard preconditioners. Indeed, popularization of the first-order system least-squares methodology (FOSLS) was driven by the applicability of geometric and algebraic multigrid to otherwise indefinite problems. However, for wave propagation problems, FOSLS is known to be highly dissipative and is thus less competitive in the high-frequency regime. The discontinuous Petrov–Galerkin (DPG) method of Demkowicz and Gopalakrishnan is a minimum residual finite element method with several additional attractive properties: mesh-independent stability, a built-in error indicator, and applicability to a number of variational formulations. In the context of high-frequency wave propagation, the ultraweak DPG formulation has been observed to produce pollution error roughly commensurate to Galerkin discretizations. DPG discretizations may thus deliver accuracy typical of classical discretization techniques, but result in Hermitian positive-definite discrete systems that are often more amenable to preconditioning. A multigrid preconditioner for DPG systems, developed in the dissertation work of S. Petrides, was shown to scale efficiently in a shared-memory implementation. The primary objective of this dissertation is development of an efficient, distributed implementation of the DPG multigrid solver (DPG-MG). The distributed DPG-MG solver developed in this work will be demonstrated to be massively scalable, enabling solution of three-dimensional problems with O(10¹²) degrees of freedom on up to 460 000 CPU cores, an unprecedented scale for high-frequency wave propagation. The scalability of the DPG-MG solver will be further combined with hp-adaptivity to enable efficient solution of challenging real-world high-frequency wave propagation problems including optical fiber modeling, simulation of RF heating in tokamak devices, and seismic simulation. These applications include complex three-dimensional geometries, heterogeneous and anisotropic media, and localized features; demonstrating the robustness and versatility of the solver and tools developed in this dissertation. 

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