Changsheng Zhu1,2,*, Jintao Miao1, Zihao Gao3,*, Shuo Liu1, Jingjie Li1
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4313-4340, 2025, DOI:10.32604/cmc.2025.067157
- 30 July 2025
Abstract As the demand for advanced material design and performance prediction continues to grow, traditional phase-field models are increasingly challenged by limitations in computational efficiency and predictive accuracy, particularly when addressing high-dimensional and complex data in multicomponent systems. To overcome these challenges, this study proposes an innovative model, LSGWO-BP, which integrates an improved Grey Wolf Optimizer (GWO) with a backpropagation neural network (BP) to enhance the accuracy and efficiency of quasi-phase equilibrium predictions within the KKS phase-field framework. Three mapping enhancement strategies were investigated–Circle-Root, Tent-Cosine, and Logistic-Sine mappings–with the Logistic mapping further improved via Sine perturbation… More >
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