We consider the data-driven acceleration of Galerkin-based finite element discretizations for the approximation of partial differential equations (PDEs) [1]. The aim is to obtain approximations on meshes that are very coarse, but nevertheless resolve quantities of interest with striking accuracy.Our work is inspired by the the machine learning framework of Mishra [2], who considered the data-driven acceleration of finite-difference schemes. The essential idea is to optimize a numerical method for a given coarse mesh, by minimizing a loss function consisting of errors with respect to the quantities of interest for obtained training data.
EWCO accumulated a long list of online first articles over the years. To accommodate these backlogs, EWCO published six regular issues and five supplementary issues in Volume 38 during 2022, with about 50 articles in each issue. According to the Web of Science Journal Citation Reports, the impact factor of EWCO has increased to 8.083 ( 2021) and the number of downloads reached 222,369 (2021).Two special issues have been published in Volume 38, including Image-Based Methods in Computational Medicine by Adrian Buganza Tepole, Rafael Grytz, Maria Holland, and Johannes Weickenmeier (Issue 5); and Numerical Simulation for Additive Manufacturing Processes and Products by Alessandro Reali, Ferdinando Auricchio, Michele Chiumenti, and Ernst Rank (Issue 6). In addition, the special issue of Computational Modeling Based on Nonlocal Theory by Timon Rabczuk, Erkan Oterkus, and Xiaoying Zhuang is ready and will be published in 2023. The special issue of UKACM 2022: Advances in Computational Mechanics by Jelena Ninic, Kristoffer van der Zee, Matteo Icardi, and Fangying Wang is on-going and will be completed in 2023.We approved two new special issue proposals which will
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