TY  - GEN
AB  - This volume includes contributions from the 9th Parallel-in-Time (PinT) workshop, an annual gathering devoted to the field of time-parallel methods, aiming to adapt existing computer models to next-generation machines by adding a new dimension of scalability. As the latest supercomputers advance in microprocessing ability, they require new mathematical algorithms in order to fully realize their potential for complex systems. The use of parallel-in-time methods will provide dramatically faster simulations in many important areas, including biomedical (e.g., heart modeling), computational fluid dynamics (e.g., aerodynamics and weather prediction), and machine learning applications. Computational and applied mathematics is crucial to this progress, as it requires advanced methodologies from the theory of partial differential equations in a functional analytic setting, numerical discretization and integration, convergence analyses of iterative methods, and the development and implementation of new parallel algorithms. Therefore, the workshop seeks to bring together an interdisciplinary group of experts across these fields to disseminate cutting-edge research and facilitate discussions on parallel time integration methods.
AU  - Ong, Benjamin,
AU  - Schroder, Jacob,
AU  - Shipton, Jemma,
AU  - Friedhoff, Stephanie,
CN  - QA76.5
CY  - Cham, Switzerland :
DA  - 2021.
DO  - 10.1007/978-3-030-75933-9
DO  - doi
ID  - 1439172
KW  - Parallel algorithms
KW  - Computer simulation
KW  - Algorithmes parallèles
KW  - Simulation par ordinateur
LK  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-75933-9
N2  - This volume includes contributions from the 9th Parallel-in-Time (PinT) workshop, an annual gathering devoted to the field of time-parallel methods, aiming to adapt existing computer models to next-generation machines by adding a new dimension of scalability. As the latest supercomputers advance in microprocessing ability, they require new mathematical algorithms in order to fully realize their potential for complex systems. The use of parallel-in-time methods will provide dramatically faster simulations in many important areas, including biomedical (e.g., heart modeling), computational fluid dynamics (e.g., aerodynamics and weather prediction), and machine learning applications. Computational and applied mathematics is crucial to this progress, as it requires advanced methodologies from the theory of partial differential equations in a functional analytic setting, numerical discretization and integration, convergence analyses of iterative methods, and the development and implementation of new parallel algorithms. Therefore, the workshop seeks to bring together an interdisciplinary group of experts across these fields to disseminate cutting-edge research and facilitate discussions on parallel time integration methods.
PB  - Springer,
PP  - Cham, Switzerland :
PY  - 2021.
SN  - 9783030759339
SN  - 3030759334
T1  - Parallel-in-Time integration methods :9th Parallel-in-Time Workshop, June 8-12, 2020 /
TI  - Parallel-in-Time integration methods :9th Parallel-in-Time Workshop, June 8-12, 2020 /
UR  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-75933-9
VL  - v. 356
ER  -