We have presented a maximum principle preserving the unconditionally stable scheme for the Allen-Cahn (AC) equation with a high-order polynomial potential. The proposed method ensures the preservation of the maximum principle, a critical characteristic for accurately modeling phase transitions and maintaining physical consistency in simulations. The proposed method uses an operator splitting technique, a numerical approach that decomposes a complex problem into simpler subproblems, solved sequentially, to improve computational efficiency and stability. The operator splitting method applied to the AC equation yields one nonlinear equation and several linear equations. To solve the nonlinear equation, we applied the frozen coefficient method, which approximates variable coefficients in differential equations by treating them as constants within small regions, simplifies the problem, and enables more efficient numerical solutions. For several linear equations, which are diffusion equations, we applied a fully implicit finite difference scheme to obtain unconditional stability. By using these methods, we achieved unconditional stability for the AC equation. To validate the superior performance of the developed algorithm, we performed computational tests. Computational experiments demonstrated its unconditional stability, particularly in handling high- order polynomial potentials. Furthermore, we highlighted a distinctive feature of the AC equation in modeling phase separation under noisy data conditions.
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Jiangsu Normal Univ, Sch Math & Stat, Xuzhou 221116, Jiangsu, Peoples R ChinaJiangsu Normal Univ, Sch Math & Stat, Xuzhou 221116, Jiangsu, Peoples R China
Hou, Dianming
Zhang, Tianxiang
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Jiangsu Normal Univ, Sch Math & Stat, Xuzhou 221116, Jiangsu, Peoples R ChinaJiangsu Normal Univ, Sch Math & Stat, Xuzhou 221116, Jiangsu, Peoples R China
Zhang, Tianxiang
Zhu, Hongyi
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Jinan Univ Zhuhai Campus, Sch Intelligent Syst Sci & Engn, Zhuhai 519070, Peoples R ChinaJiangsu Normal Univ, Sch Math & Stat, Xuzhou 221116, Jiangsu, Peoples R China
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Purdue Univ, Dept Math, W Lafayette, IN 47907 USAPurdue Univ, Dept Math, W Lafayette, IN 47907 USA
Shen, Jie
Tang, Tao
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South Univ Sci & Technol, Dept Math, Shenzhen 518055, Guangdong, Peoples R China
Hong Kong Baptist Univ, Dept Math, Kowloon Tong, Hong Kong, Peoples R China
Hong Kong Baptist Univ, Inst Computat & Theoret Studies, Kowloon Tong, Hong Kong, Peoples R ChinaPurdue Univ, Dept Math, W Lafayette, IN 47907 USA
Tang, Tao
Yang, Jiang
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Columbia Univ, Dept Appl Phys & Appl Math, New York, NY 10027 USAPurdue Univ, Dept Math, W Lafayette, IN 47907 USA
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Beijing Normal Univ, Lab Math & Complex Syst, Beijing 100875, Peoples R China
Beijing Normal Univ, Sch Math Sci, Beijing 100875, Peoples R ChinaBeijing Normal Univ, Lab Math & Complex Syst, Beijing 100875, Peoples R China
Li, Jingwei
Ju, Lili
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Univ South Carolina, Dept Math, Columbia, SC 29208 USABeijing Normal Univ, Lab Math & Complex Syst, Beijing 100875, Peoples R China
Ju, Lili
Cai, Yongyong
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Beijing Normal Univ, Lab Math & Complex Syst, Beijing 100875, Peoples R China
Beijing Normal Univ, Sch Math Sci, Beijing 100875, Peoples R ChinaBeijing Normal Univ, Lab Math & Complex Syst, Beijing 100875, Peoples R China
Cai, Yongyong
Feng, Xinlong
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Xinjiang Univ, Coll Math & Syst Sci, Urumqi 830046, Peoples R ChinaBeijing Normal Univ, Lab Math & Complex Syst, Beijing 100875, Peoples R China