Merge pull request #815 from Parallel-in-Time/bibtex-bibbot-777-9433106

pint.bib updates

Author: pancetta

_bibliography/pint.bib

@@ -5931,6 +5931,19 @@ @article{LeeEtAl2022
 	year = {2022},
 }
 
+@inbook{LeeEtAl2022b,
+	author = {Lee, Chang-Ock and Lee, Youngkyu and Park, Jongho},
+	booktitle = {Lecture Notes in Computational Science and Engineering},
+	doi = {10.1007/978-3-030-95025-5_43},
+	isbn = {9783030950255},
+	issn = {2197-7100},
+	pages = {407–415},
+	publisher = {Springer International Publishing},
+	title = {A Parareal Architecture for Very Deep Convolutional Neural Networks},
+	url = {http://dx.doi.org/10.1007/978-3-030-95025-5_43},
+	year = {2022},
+}
+
 @article{LegollEtAl2022,
 	author = {Fr{\'{e}}d{\'{e}}ric Legoll and Tony Leli{\`{e}}vre and Upanshu Sharma},
 	doi = {10.1137/21m1412979},
@@ -6995,6 +7008,15 @@ @article{JanssensEtAl2024
 	year = {2024},
 }
 
+@unpublished{KaiserEtAl2024,
+	abstract = {Recent advances in wave modeling use sufficiently accurate fine solver outputs to train a neural network that enhances the accuracy of a fast but inaccurate coarse solver. In this paper we build upon the work of Nguyen and Tsai (2023) and present a novel unified system that integrates a numerical solver with a deep learning component into an end-to-end framework. In the proposed setting, we investigate refinements to the network architecture and data generation algorithm. A stable and fast solver further allows the use of Parareal, a parallel-in-time algorithm to correct high-frequency wave components. Our results show that the cohesive structure improves performance without sacrificing speed, and demonstrate the importance of temporal dynamics, as well as Parareal, for accurate wave propagation.},
+	author = {Luis Kaiser and Richard Tsai and Christian Klingenberg},
+	howpublished = {arXiv:2402.02304v4 [math.AP]},
+	title = {Efficient Numerical Wave Propagation Enhanced By An End-to-End Deep Learning Model},
+	url = {http://arxiv.org/abs/2402.02304v4},
+	year = {2024},
+}
+
 @article{Kumar2024,
 	author = {Kumar, Ajit},
 	doi = {10.1109/tcsii.2024.3381372},