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研究生: 鄭博文
Zheng, Bo-Wen
論文名稱: 結合深度學習與模擬退火法之寬帶隙聲子晶體設計
Design of Wide-Bandgap Phononic Crystals Combining Deep Learning and Simulated Annealing
指導教授: 張怡玲
Chang, I-Ling
學位類別: 碩士
Master
系所名稱: 工學院 - 機械工程學系
Department of Mechanical Engineering
論文出版年: 2025
畢業學年度: 113
語文別: 中文
論文頁數: 118
中文關鍵詞: 聲子晶體拓樸最佳化深度學習模型模擬退火法
外文關鍵詞: phononic crystal, topology optimization, deep learning model, simulated annealing
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  • 摘要 I Abstract II 誌謝 XVI 目錄 XVII 表目錄 XIX 圖目錄 XX 第一章 緒論 1 1.1 前言 1 1.2 文獻回顧 2 1.2.1 聲子晶體 2 1.2.2 拓樸最佳化 3 1.2.3 深度學習 4 1.3 研究動機 6 1.4 本文架構 7 第二章 基本理論與方法 8 2.1 聲子晶體 8 2.1.1 倒晶格(Reciprocal lattice) 8 2.1.2 第一布里淵區(Brillouin zone) 11 2.1.3 布洛赫定理(Bloch’s Theorem) 12 2.1.4 聲子晶體帶隙的數值計算方法 13 2.2 拓樸最佳化-模擬退火法 16 2.2.1 模擬退火法基本介紹 16 2.2.2 模擬退火法流程 16 2.3 卷積神經網路 19 2.3.1 卷積層 19 2.3.2 池化層 21 2.3.3 全連接層 22 2.3.4 損失函數 23 2.3.5 激活函數 24 2.3.6 批次標準化 27 第三章 聲子晶體色散曲線與帶隙之深度學習預測 29 3.1 深度學習模型之數據集 29 3.2 深度學習模型架構與設置 34 3.3 聲子晶體色散曲線預測模型之準確性分析 37 3.4 聲子晶體帶隙預測模型之準確性分析 49 第四章 結合深度學習模型與模擬退火法之聲子晶體帶隙最佳化 54 4.1 模擬退火法與深度學習模型的組合 54 4.2 數據集大小對於最佳化的影響 56 4.3 模擬退火法之連通率影響 68 4.4 結果與討論 70 第五章 結論與未來展望 72 5.1 結論 72 5.2 未來展望 73 參考文獻 74 附錄 78 A. 色散曲線順序判斷 78 B. 色散曲線之預測誤差分布 80 C. 不同填充率之最佳化結果 86 D. 不同初始值之帶隙最佳化結果比較 90

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