Merge pull request #995 from Parallel-in-Time/bibtex-arxivbot-987-d34c100

pint.bib updates

Author: tlunet

_bibliography/pint.bib

@@ -6853,6 +6853,15 @@ @unpublished{ZhouEtAl2023b
 	year = {2023},
 }
 
+@unpublished{AppelEtAl2024,
+	abstract = {This paper presents a method of performing topology optimisation of transient heat conduction problems using the parallel-in-time method Parareal. To accommodate the adjoint analysis, the Parareal method was modified to store intermediate time steps. Preliminary tests revealed that Parareal requires many iterations to achieve accurate results and, thus, achieves no appreciable speedup. To mitigate this, a one-shot approach was used, where the time history is iteratively refined over the optimisation process. The method estimates objectives and sensitivities by introducing cumulative objectives and sensitivities and solving for these using a single iteration of Parareal, after which it updates the design using the Method of Moving Asymptotes. The resulting method was applied to a test problem where a power mean of the temperature was minimised. It achieved a peak speedup relative to a sequential reference method of $5\times$ using 16 threads. The resulting designs were similar to the one found by the reference method, both in terms of objective values and qualitative appearance. The one-shot Parareal method was compared to the Parallel Local-in-Time method of topology optimisation. This revealed that the Parallel Local-in-Time method was unstable for the considered test problem, but it achieved a peak speedup of $12\times$ using 32 threads. It was determined that the dominant bottleneck in the one-shot Parareal method was the time spent on computing coarse propagators.},
+	author = {Magnus Appel and Joe Alexandersen},
+	howpublished = {arXiv:2411.19030v1 [cs.CE]},
+	title = {One-shot Parareal Approach for Topology Optimisation of Transient Heat Flow},
+	url = {http://arxiv.org/abs/2411.19030v1},
+	year = {2024},
+}
+
 @article{BaumannEtAl2024,
 	abstract = {Spectral Deferred Correction (SDC) is an iterative method for the numerical solution of ordinary differential equations. It works by refining the numerical solution for an initial value problem by approximately solving differential equations for the error, and can be interpreted as a preconditioned fixed-point iteration for solving the fully implicit collocation problem. We adopt techniques from embedded Runge-Kutta Methods (RKM) to SDC in order to provide a mechanism for adaptive time step size selection and thus increase computational efficiency of SDC. We propose two SDC-specific estimates of the local error that are generic and do not rely on problem specific quantities. We demonstrate a gain in efficiency over standard SDC with fixed step size and compare efficiency favorably against state-of-the-art adaptive RKM.},
 	author = {Baumann, Thomas and G{\"o}tschel, Sebastian and Lunet, Thibaut and Ruprecht, Daniel and Speck, Robert},
@@ -7681,12 +7690,12 @@ @unpublished{AlexandersenEtAl2025
 	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},
-	howpublished = {arXiv:2505.10168v1 [cs.CE]},
+@article{AppelEtAl2025,
+	author = {Appel, Magnus and Alexandersen, Joe},
+	doi = {10.2139/ssrn.5256438},
+	publisher = {Elsevier BV},
 	title = {Space-Time Multigrid Methods Suitable for Topology Optimisation of Transient Heat Conduction},
-	url = {http://arxiv.org/abs/2505.10168v1},
+	url = {http://dx.doi.org/10.2139/ssrn.5256438},
 	year = {2025},
 }
 
@@ -8368,12 +8377,3 @@ @article{HeEtAl2026
 	volume = {152},
 	year = {2026},
 }
-
-@unpublished{AppelEtAl2024,
-	abstract = {This paper presents a method of performing topology optimisation of transient heat conduction problems using the parallel-in-time method Parareal. To accommodate the adjoint analysis, the Parareal method was modified to store intermediate time steps. Preliminary tests revealed that Parareal requires many iterations to achieve accurate results and, thus, achieves no appreciable speedup. To mitigate this, a one-shot approach was used, where the time history is iteratively refined over the optimisation process. The method estimates objectives and sensitivities by introducing cumulative objectives and sensitivities and solving for these using a single iteration of Parareal, after which it updates the design using the Method of Moving Asymptotes. The resulting method was applied to a test problem where a power mean of the temperature was minimised. It achieved a peak speedup relative to a sequential reference method of $5\times$ using 16 threads. The resulting designs were similar to the one found by the reference method, both in terms of objective values and qualitative appearance. The one-shot Parareal method was compared to the Parallel Local-in-Time method of topology optimisation. This revealed that the Parallel Local-in-Time method was unstable for the considered test problem, but it achieved a peak speedup of $12\times$ using 32 threads. It was determined that the dominant bottleneck in the one-shot Parareal method was the time spent on computing coarse propagators.},
-	author = {Magnus Appel and Joe Alexandersen},
-	howpublished = {arXiv:2411.19030v1 [cs.CE]},
-	title = {One-shot Parareal Approach for Topology Optimisation of Transient Heat Flow},
-	url = {http://arxiv.org/abs/2411.19030v1},
-	year = {2024},
-}