Merge pull request #863 from Parallel-in-Time/bibtex-bibbot-862-2febd86

pint.bib updates

_bibliography/pint.bib

@@ -7093,6 +7093,18 @@ @unpublished{IbrahimEtAl2024
 	year = {2024},
 }
 
+@inproceedings{IqbalEtAl2024,
+	author = {Iqbal, Sahel and Abdulsamad, Hany and Cator, Tripp and Braga-Neto, Ulisses and Särkkä, Simo},
+	booktitle = {2024 IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP)},
+	doi = {10.1109/mlsp58920.2024.10734739},
+	month = {September},
+	pages = {1–6},
+	publisher = {IEEE},
+	title = {Parallel-in-Time Probabilistic Solutions for Time-Dependent Nonlinear Partial Differential Equations},
+	url = {http://dx.doi.org/10.1109/MLSP58920.2024.10734739},
+	year = {2024},
+}
+
 @unpublished{JackamanEtAl2024,
 	abstract = {Space-time finite-element discretizations are well-developed in many areas of science and engineering, but much work remains within the development of specialized solvers for the resulting linear and nonlinear systems. In this work, we consider the all-at-once solution of the discretized Navier-Stokes equations over a space-time domain using waveform relaxation multigrid methods. In particular, we show how to extend the efficient spatial multigrid relaxation methods from [37] to a waveform relaxation method, and demonstrate the efficiency of the resulting monolithic Newton-Krylov-multigrid solver. Numerical results demonstrate the scalability of the solver for varying discretization order and physical parameters.},
 	author = {James Jackaman and Scott MacLachlan},