The scalar auxiliary variable (SAV) approach for gradient flows

By Jie Shen, Jie Xu, Jiang Yang. Journal of Computational Physics, 2018

1,129 citations recorded by the source corpus.

Subfields: Computational Mechanics, Materials Chemistry, Numerical Analysis

View Paper: https://www.semanticscholar.org/paper/e73e97d56e5c100c0e5cda99660aeaba3b9f9480

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. Xiaobing H. Feng
2. Qiang Du
3. Jie Xu
4. Jie Shen
5. Xiaofeng Yang
6. Jia Zhao
7. Qi Wang
8. Jiang Yang

In Materials Chemistry

1. Xiaobing H. Feng
2. Qiang Du
3. Jie Shen
4. Jie Xu
5. Xiaofeng Yang
6. Hao Wu
7. Junseok Kim
8. Jia Zhao

In Numerical Analysis

1. Qiang Du
2. Xiaobing H. Feng
3. Jie Shen
4. Jie Xu
5. Xiaofeng Yang

Citations in the corpus

• Convergence and Error Analysis for the Scalar Auxiliary Variable (SAV) Schemes to Gradient Flows (2018, methodology): “1 Second-order schemes Since the second order BDF2 and Crank–Nicolson schemes [17, 18] also enjoy the unconditional energy stability similar to (1.”
• The phase field method for geometric moving interfaces and their numerical approximations (2019, positive): “First, in order to preserve the gradient flow structure at the discrete level, it is desirable to ensure the numerical method to be energy stable besides to be accurate, which is a non-trivial task, especially if one aims to have high order (time) schemes (see [436, 440, 382] for some recent advance”