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1. Declarative Specification for Unstructured Mesh Editing Algorithms

   https://doi.org/10.1145/3550454.3555513  

   Jiang, Zhongshi; Dai, Jiacheng; Hu, Yixin; Zhou, Yunfan; Dumas, Jeremie; Zhou, Qingnan; Bajwa, Gurkirat Singh; Zorin, Denis; Panozzo, Daniele; Schneider, Teseo (December 2022, ACM Transactions on Graphics)

   We introduce a novel approach to describe mesh generation, mesh adaptation, and geometric modeling algorithms relying on changing mesh connectivity using a high-level abstraction. The main motivation is to enable easy customization and development of these algorithms via a declarative specification consisting of a set of per-element invariants, operation scheduling, and attribute transfer for each editing operation. We demonstrate that widely used algorithms editing surfaces and volumes can be compactly expressed with our abstraction, and their implementation within our framework is simple, automatically parallelizable on shared-memory architectures, and with guaranteed satisfaction of the prescribed invariants. These algorithms are readable and easy to customize for specific use cases. We introduce a software library implementing this abstraction and providing automatic shared-memory parallelization. 

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2. GNN-Surrogate: A Hierarchical and Adaptive Graph Neural Network for Parameter Space Exploration of Unstructured-Mesh Ocean Simulations

   https://doi.org/10.1109/TVCG.2022.3165345  

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3. Algorithms for Parallel Generic hp -Adaptive Finite Element Software

   https://doi.org/10.1145/3603372  

   Fehling, Marc; Bangerth, Wolfgang (September 2023, ACM Transactions on Mathematical Software)

   na (Ed.)

   Thehp-adaptive finite element method—where one independently chooses the mesh size (h) and polynomial degree (p) to be used on each cell—has long been known to have better theoretical convergence properties than eitherh- orp-adaptive methods alone. However, it is not widely used, owing at least in part to the difficulty of the underlying algorithms and the lack of widely usable implementations. This is particularly true when used with continuous finite elements. Herein, we discuss algorithms that are necessary for a comprehensive and generic implementation ofhp-adaptive finite element methods on distributed-memory, parallel machines. In particular, we will present a multistage algorithm for the unique enumeration of degrees of freedom suitable for continuous finite element spaces, describe considerations for weighted load balancing, and discuss the transfer of variable size data between processes. We illustrate the performance of our algorithms with numerical examples and demonstrate that they scale reasonably up to at least 16,384 message passage interface processes. We provide a reference implementation of our algorithms as part of the open source librarydeal.II. 

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4. Accelerating Unstructured Mesh Point Location With RT Cores

   https://doi.org/10.1109/TVCG.2020.3042930  

   Morrical, Nate; Wald, Ingo; Usher, Will; Pascucci, Valerio (August 2022, IEEE Transactions on Visualization and Computer Graphics)
5. HAZniCS – Software Components for Multiphysics Problems

   https://doi.org/10.1145/3625561  

   Budiša, Ana; Hu, Xiaozhe; Kuchta, Miroslav; Mardal, Kent-André; Zikatanov, Ludmil T (December 2023, ACM Transactions on Mathematical Software)

   We introduce the software toolbox HAZniCS for solving interface-coupled multiphysics problems. HAZniCS is a suite of modules that combines the well-known FEniCS framework for finite element discretization with solver and graph library HAZmath. The focus of this article is on the design and implementation of robust and efficient solver algorithms which tackle issues related to the complex interfacial coupling of the physical problems often encountered in applications in brain biomechanics. The robustness and efficiency of the numerical algorithms and methods is shown in several numerical examples, namely the Darcy-Stokes equations that model the flow of cerebrospinal fluid in the human brain and the mixed-dimensional model of electrodiffusion in the brain tissue. 

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