pint.bib updates

_bibliography/pint.bib

@@ -7685,6 +7685,15 @@ @article{AlesEtAl2025
 	year = {2025},
 }
 
+@unpublished{AlexandersenEtAl2025,
+	abstract = {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.},
+	author = {Joe Alexandersen and Magnus Appel},
+	howpublished = {arXiv:2508.09589v1 [cs.CE]},
+	title = {Large-Scale Topology Optimisation of Time-dependent Thermal Conduction Using Space-Time Finite Elements and a Parallel Space-Time Multigrid Preconditioner},
+	url = {http://arxiv.org/abs/2508.09589v1},
+	year = {2025},
+}
+
 @unpublished{AppelEtAl2025,
 	abstract = {This paper presents Space-Time MultiGrid (STMG) methods which are suitable for performing topology optimisation of transient heat conduction problems. The proposed methods use a pointwise smoother and uniform Cartesian space-time meshes. For problems with high contrast in the diffusivity, it was found that it is beneficial to define a coarsening strategy based on the geometric mean of the minimum and maximum diffusivity. However, other coarsening strategies may be better for other smoothers. Several methods of discretising the coarse levels were tested. Of these, it was best to use a method which averages the thermal resistivities on the finer levels. However, this was likely a consequence of the fact that only one spatial dimension was considered for the test problems. A second coarsening strategy was proposed which ensures spatial resolution on the coarse grids. Mixed results were found for this strategy. The proposed STMG methods were used as a solver for a one-dimensional topology optimisation problem. In this context, the adjoint problem was also solved using the STMG methods. The STMG methods were sufficiently robust for this application, since they converged during every optimisation cycle. It was found that the STMG methods also work for the adjoint problem when the prolongation operator only sends information forwards in time, even although the direction of time for the adjoint problem is backwards.},
 	author = {Magnus Appel and Joe Alexandersen},
@@ -8214,6 +8223,18 @@ @article{WangEtAl2025b
 	year = {2025},
 }
 
+@inproceedings{YodaEtAl2025,
+	author = {Yoda, Ryo and Bolten, Matthias},
+	booktitle = {2025 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)},
+	doi = {10.1109/ipdpsw66978.2025.00063},
+	month = {June},
+	pages = {375–384},
+	publisher = {IEEE},
+	title = {Block Epsilon-Circulant Preconditioning with GPU-Accelerated Spatial Solvers for Linear Time-Dependent PDEs},
+	url = {http://dx.doi.org/10.1109/IPDPSW66978.2025.00063},
+	year = {2025},
+}
+
 @article{ZengEtAl2025,
 	author = {Zeng, Xianfu and Song, Haiyan},
 	doi = {10.1016/j.matcom.2025.02.007},