| 研究生: |
馬裕傑 Ma, Yu-Jie |
|---|---|
| 論文名稱: |
利用純量輔助變量法模擬福克-普朗克方程式 The Scalar Auxiliary Variable method for the numerical simulation of the Fokker-Planck equation |
| 指導教授: |
陳旻宏
Chen, Min-Hung |
| 學位類別: |
碩士 Master |
| 系所名稱: |
理學院 - 數學系應用數學碩博士班 Department of Mathematics |
| 論文出版年: | 2022 |
| 畢業學年度: | 110 |
| 語文別: | 英文 |
| 論文頁數: | 21 |
| 中文關鍵詞: | 福克-普朗克方程式 、純量輔助變量法 、梯度流 、數值運算 |
| 外文關鍵詞: | Fokker-Planck equation, Scalar Auxiliary Variable method, Gradient flow, Numerical computations |
| 相關次數: | 點閱:71 下載:13 |
| 分享至: |
| 查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
在本篇論文中,我們利用純量輔助變量法去模擬福克-普朗克方程式,純量輔助變量法用於修改與系統相關的能量,使其在離散水平上單調遞減,為梯度流模型構造出一個無條件能量穩定的數值方案,由此產生的數值方案是高效且易於實施的。我們對此數值方案採用二階有限差分法對空間作離散化,時間上使用前向歐拉法獲得顯式方案。在數值實驗方面,我們得到了預期的收斂階數,並比較在不同時間步長下修改前後能量的差異,我們還證明了在此數值結構下仍保持質量守恆和能量耗散這兩種性質
In this paper, we apply the scalar auxiliary variable method to simulate the Fokker-Planck equation. The SAV method is used to modify the free energy associated with the system such that it decreases monotonically at the discrete level to construct an unconditionally energy stable numerical scheme for the gradient flow model, the resulting numerical scheme is efficient and easy to implement. For this numerical scheme, we use the second-order finite difference method to discretize the space, and use the forward Euler method to obtain an explicit scheme in time. In numerical experiments, we obtain the expected order of convergence and compare the difference between the modified and original energy at different time steps, we also prove that both properties of mass conservation and energy dissipation are preserved under this numerical structure
[1] Shen, Jie, Jie Xu, and Jiang Yang. ”The scalar auxiliary variable (SAV) approach for gradient flows.”Journal of Computational Physics 353 (2018): 407-416.
[2] Jordan, Richard, David Kinderlehrer, and Felix Otto. ”The variational formulation of the Fokker–Planck equation.”SIAM journal on mathematical analysis 29.1 (1998): 1-17.
[3] Poulain, Alexandre. ”Scalar auxiliary variable finite element scheme for the parabolic-parabolic Keller-Segel model.”arXiv preprint arXiv:2007.01601 (2020).
[4] Huang, Fukeng, and Jie Shen. ”Bound/Positivity preserving and energy stable SAV schemes for dissipative systems: Applications to Keller-Segel and Poisson-Nernst-Planck equations.” SIAM J. Sci. Comput 43.3 (2021).
[5] Pavliotis, Grigorios A. ”Stochastic processes and applications.”
Informe técnico (2015).
[6] Shen, Jie, Jie Xu, and Jiang Yang. ”A new class of efficient and robust energy stable schemes for gradient flows.” SIAM Review 61. (2019): 474-506.
[7] Shen, Jie, and Jie Xu. ”Convergence and error analysis for the scalar auxiliary variable (SAV) schemes to gradient flows.” SIAM Journal on Numerical Analysis 56.5 (2018): 2895-2912.
[8] Hu, Jingwei, and Xiangxiong Zhang. ”Positivity-preserving and energy-dissipative finite difference schemes for the Fokker-Planck and Keller-Segel equations.”arXiv preprint arXiv:2103.16790 (2021).
[9] Liu, Shu, et al. ”Neural parametric fokker-planck equations.” arXiv preprint arXiv:2002.11309 (2020).
[10] Qian, Yiran, Zhongming Wang, and Shenggao Zhou. ”A conservative, free energy dissipating, and positivity preserving finite difference scheme for multi-dimensional nonlocal Fokker–Planck equation.”Journal of Computational Physics 386 (2019): 22-36.
[11] Kumar, Pankaj, and S. Narayanan. ”Solution of Fokker-Planck equation by finite element and finite difference methods for nonlinear systems.” Sadhana 31.4 (2006): 445-461.
[12] Chang, J. S., and G. Cooper. ”A practical difference scheme for Fokker-Planck equations.” Journal of Computational Physics 6.1 (1970): 1-16.
[13] Ambrosio, Luigi, Nicola Gigli, and Giuseppe Savaré. Gradient flows: in metric spaces and in the space of probability measures. Springer Science & Business Media, 2005.
[14] Villani, Cédric. Topics in optimal transportation. Vol. 58. American Mathematical Soc., 2021.