From 18144bbd0e0218cf8e3bc57c2c21f4123c17e33b Mon Sep 17 00:00:00 2001 From: danielru Date: Tue, 7 Nov 2023 06:50:28 +0000 Subject: [PATCH] updated pint.bib using bibbot --- _bibliography/pint.bib | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/_bibliography/pint.bib b/_bibliography/pint.bib index e9cfa9d3..b71437c7 100644 --- a/_bibliography/pint.bib +++ b/_bibliography/pint.bib @@ -6279,6 +6279,19 @@ @inproceedings{BarmanEtAl2023 year = {2023}, } +@article{BoltenEtAl2023, + author = {Matthias Bolten and Stephanie Friedhoff and Jens Hahne}, + doi = {10.1016/j.parco.2023.103050}, + journal = {Parallel Computing}, + month = {nov}, + pages = {103050}, + publisher = {Elsevier {BV}}, + title = {Task graph-based performance analysis of parallel-in-time methods}, + url = {https://doi.org/10.1016/j.parco.2023.103050}, + volume = {118}, + year = {2023}, +} + @unpublished{BoschEtAl2023, abstract = {Probabilistic numerical solvers for ordinary differential equations (ODEs) treat the numerical simulation of dynamical systems as problems of Bayesian state estimation. Aside from producing posterior distributions over ODE solutions and thereby quantifying the numerical approximation error of the method itself, one less-often noted advantage of this formalism is the algorithmic flexibility gained by formulating numerical simulation in the framework of Bayesian filtering and smoothing. In this paper, we leverage this flexibility and build on the time-parallel formulation of iterated extended Kalman smoothers to formulate a parallel-in-time probabilistic numerical ODE solver. Instead of simulating the dynamical system sequentially in time, as done by current probabilistic solvers, the proposed method processes all time steps in parallel and thereby reduces the span cost from linear to logarithmic in the number of time steps. We demonstrate the effectiveness of our approach on a variety of ODEs and compare it to a range of both classic and probabilistic numerical ODE solvers.}, author = {Nathanael Bosch and Adrien Corenflos and Fatemeh Yaghoobi and Filip Tronarp and Philipp Hennig and Simo Särkkä},