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AlexandersenEtAl2025
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<span id="AlexandersenEtAl2025">J. Alexandersen and M. Appel, “Large-Scale Topology Optimisation of Time-dependent Thermal Conduction Using Space-Time Finite Elements and a Parallel Space-Time Multigrid Preconditioner,” arXiv:2508.09589v1 [cs.CE], 2025 [Online]. Available at: <a href="http://arxiv.org/abs/2508.09589v1" target="_blank">http://arxiv.org/abs/2508.09589v1</a></span>
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BibTeX entry <code>AlexandersenEtAl2025</code>
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<pre class="abstract">@unpublished{AlexandersenEtAl2025,
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author = {Alexandersen, Joe and Appel, Magnus},
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howpublished = {arXiv:2508.09589v1 [cs.CE]},
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title = {Large-Scale Topology Optimisation of Time-dependent Thermal Conduction Using Space-Time Finite Elements and a Parallel Space-Time Multigrid Preconditioner},
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url = {http://arxiv.org/abs/2508.09589v1},
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year = {2025}
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}
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This paper presents a novel space-time topology optimisation framework for time-dependent thermal conduction problems, aiming to significantly reduce the time-to-solution. By treating time as an additional spatial dimension, we discretise the governing equations using a stabilised continuous Galerkin space-time finite element method. The resulting large all-at-once system is solved using an iterative Krylov solver preconditioned with a parallel space-time multigrid method employing a semi-coarsening strategy. Implemented in a fully parallel computing framework, the method yields a parallel-in-time method that demonstrates excellent scalability on a distributed-memory supercomputer, solving problems up to 4.2 billion degrees of freedom. Comparative studies show up to 52x speed-up over traditional time-stepping approaches, with only moderate increases in total computational cost in terms of core-hours. The framework is validated on benchmark problems with both time-constant and time-varying designs, and its flexibility is demonstrated through variations in material properties. These results establish the proposed space-time method as a promising approach for large-scale time-dependent topology optimisation in thermal applications.
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<span id="YodaEtAl2025">R. Yoda and M. Bolten, “Block Epsilon-Circulant Preconditioning with GPU-Accelerated Spatial Solvers for Linear Time-Dependent PDEs,” in <i>2025 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)</i>, 2025, pp. 375–384, doi: 10.1109/ipdpsw66978.2025.00063 [Online]. Available at: <a href="http://dx.doi.org/10.1109/IPDPSW66978.2025.00063" target="_blank">http://dx.doi.org/10.1109/IPDPSW66978.2025.00063</a></span>
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<pre class="abstract">@inproceedings{YodaEtAl2025,
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author = {Yoda, Ryo and Bolten, Matthias},
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booktitle = {2025 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)},
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doi = {10.1109/ipdpsw66978.2025.00063},
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month = jun,
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pages = {375–384},
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publisher = {IEEE},
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title = {Block Epsilon-Circulant Preconditioning with GPU-Accelerated Spatial Solvers for Linear Time-Dependent PDEs},
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url = {http://dx.doi.org/10.1109/IPDPSW66978.2025.00063},
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