T
Timo Heister
Researcher at Clemson University
Publications - 68
Citations - 3567
Timo Heister is an academic researcher from Clemson University. The author has contributed to research in topics: Finite element method & Adaptive mesh refinement. The author has an hindex of 24, co-authored 62 publications receiving 2537 citations. Previous affiliations of Timo Heister include University of Göttingen & University of Utah.
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A primal-dual active set method and predictor-corrector mesh adaptivity for computing fracture propagation using a phase-field approach
TL;DR: In this paper, a primal-dual active set strategy is proposed to enforce crack irreversibility as a constraint, which can be identified as a semi-smooth Newton method, and the active set iteration is merged with the Newton iteration for solving the fully-coupled nonlinear partial differential equation discretized using finite elements.
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High accuracy mantle convection simulation through modern numerical methods
TL;DR: In this paper, the first author was supported by the Graduate School in Mathematics and Computing (FMB) at Uppsala University, Sweden and the second and third authors are supported in part through the Computational Infrastructure in Geodynamics initiative (CIG), through the National Science Foundation under Award No. KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST).
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The deal.II library, Version 8.4
Wolfgang Bangerth,Denis Davydov,Timo Heister,Luca Heltai,Guido Kanschat,Martin Kronbichler,Matthias Maier,Bruno Turcksin,David Wells +8 more
TL;DR: An overview of the new features of the finite element library deal in version 8.4.II is provided.
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The deal.II Library, Version 9.1
Daniel Arndt,Wolfgang Bangerth,Thomas C. Clevenger,Denis Davydov,Marc Fehling,Daniel Garcia-Sanchez,Graham Harper,Timo Heister,Luca Heltai,Martin Kronbichler,Ross Maguire Kynch,Matthias Maier,Jean-Paul Pelteret,Bruno Turcksin,David Wells +14 more
TL;DR: An overview of the new features of the finite element library deal, version 9.1.II is provided.
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Algorithms and data structures for massively parallel generic adaptive finite element codes
TL;DR: This work develops scalable algorithms and data structures for generic finite element methods that consider the parallel distribution of mesh data, global enumeration of degrees of freedom, constraints, and postprocessing, and removes the bottlenecks that typically limit large-scale adaptive finite element analyses.