An introduction to domain decomposition methods - algorithms, theory, and parallel implementation

By V. Dolean, P. Jolivet, F. Nataf. 2015

486 citations recorded by the source corpus.

Subfields: Computational Mechanics, Electrical and Electronic Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/734694eefa2b1cb2967e8c6a4a78e75566a3ffdc

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Ameya Dilip Jagtap
2. G. Em Karniadakis
3. Khemraj Shukla
4. Zhiping Mao
5. Maizar Raissi
6. Paris Perdikaris
7. Suchuan Dong
8. Chi-Wang Shu

In Electrical and Electronic Engineering

1. Ameya Dilip Jagtap
2. G. Em Karniadakis
3. Khemraj Shukla

In Mechanics of Materials

1. Ameya Dilip Jagtap
2. G. Em Karniadakis
3. Khemraj Shukla

Citations in the corpus

• Parallel Physics-Informed Neural Networks via Domain Decomposition (2021, methodology): “is widely used in numerical simulation for solving PDEs related to the problems of fluid flow [29], wave propagation, and heat flow [30].”