請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/100973完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 劉浩澧 | zh_TW |
| dc.contributor.advisor | Hao-Li Liu | en |
| dc.contributor.author | 洪國維 | zh_TW |
| dc.contributor.author | Guo-Wei Hong | en |
| dc.date.accessioned | 2025-11-26T16:18:42Z | - |
| dc.date.available | 2025-11-27 | - |
| dc.date.copyright | 2025-11-26 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-10-16 | - |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/100973 | - |
| dc.description.abstract | 空氣超音波陣列廣泛應用於非接觸式懸浮、物體辨識、指向性音響及觸覺合成等領域，然而這些應用的開發過程往往仰賴高成本且耗時的模擬與實驗，此外若欲結合人工智慧技術以提升系統效能，則需大量高品質資料作為訓練基礎，但資料的取得同樣面臨高昂代價與實作困難。為解決此一瓶頸，本研究提出一套基於物理引導神經算子（Physics-Informed Neural Operators, PINO）的數位孿生建構方法，針對空氣超音波陣列於三維非均質環境中的傳播行為進行模擬。我們於虛擬環境中建立傳感器陣列的數位對應模型，並訓練 PINO 模型以預測超音波在不同介質與空間分布下的三維時序聲場變化，該模型的結果能同時轉換成穩態聲場與感測器所接收之訊號，且其預測結果與真實物理行為高度一致。相較於傳統數值模擬方法，本研究所提出之 PINO 模型具備超過 20 倍的加速能力，並且能在相同硬體資源下模擬更大規模的三維場域，藉由此數位孿生平台，可快速進行多樣化的模擬實驗，亦可有效生成大量高品質資料，以支援 AI 模型之訓練與應用開發，進而大幅降低開發門檻與成本。 | zh_TW |
| dc.description.abstract | Airborne ultrasound arrays are widely utilized in applications such as non-contact levitation, object recognition, directional audio, and haptic feedback. However, the development of these applications often relies on high-cost and time-consuming simulations and experiments. Furthermore, integrating artificial intelligence to enhance system performance requires large volumes of high-quality training data, the acquisition of which is both expensive and labor-intensive. To address these challenges, this study proposes a digital twin construction framework based on Physics-Informed Neural Operators (PINO) to simulate the propagation behavior of airborne ultrasound arrays in three-dimensional heterogeneous environments. A digital counterpart of the sensor array is built within a virtual environment, and a PINO model is trained to predict the spatiotemporal evolution of the acoustic field across various media and spatial distributions. The model outputs both the steady-state sound field and the sensor-received signals, achieving high fidelity with realworld physical behavior. Compared to conventional numerical simulation methods, the proposed PINO model offers more than a 20-fold improvement in computational speed and enables the simulation of larger-scale 3D domains under the same hardware constraints. This digital twin platform facilitates rapid and diverse simulation experiments and enables the efficient generation of high-quality data to support AI model training and application development, thereby significantly reducing the development cost and complexity. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-11-26T16:18:42Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-11-26T16:18:42Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員審定書 i
致謝 ii 摘要 iii Abstract iv 目次 v 圖次 ix 表次 xiv 第一章 緒論 1 1.1 空氣超音波陣列 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 空氣超音波陣列的應用 . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 現有的超音波模擬工具 . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.4 新興的物理模擬方法 SciML . . . . . . . . . . . . . . . . . . . . . . 8 1.5 數位孿生（Digital Twin） . . . . . . . . . . . . . . . . . . . . . . . . 11 1.6 神經運算子（Neural Operator） . . . . . . . . . . . . . . . . . . . . 13 1.6.1 Fourier Neural Operator（FNO） . . . . . . . . . . . . . . . . . . 14 1.6.2 Factorized Fourier Neural Operator（F-FNO） . . . . . . . . . . . 15 1.6.3 Physics-Informed Neural Operator（PINO） . . . . . . . . . . . . 16 1.7 基於神經運算子的波傳模擬——相關文獻回顧 . . . . . . . . . . . . 18 1.7.1 Yang’s Work (2021) . . . . . . . . . . . . . . . . . . . . . . . . . . 18 1.7.2 Kong’s Work (2023) . . . . . . . . . . . . . . . . . . . . . . . . . 19 1.7.3 Lehmann’s work (2024) . . . . . . . . . . . . . . . . . . . . . . . . 20 1.8 研究目的與論文架構 . . . . . . . . . . . . . . . . . . . . . . . . . . 22 第二章 方法與理論 23 2.1 系統總覽 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.2 Physics-Informed Neural Operator 三維聲場預測 . . . . . . . . . . . . 25 2.2.1 模型輸入特徵與物理意涵 . . . . . . . . . . . . . . . . . . . . . . 26 2.2.1.1 空間與時間離散化設定 . . . . . . . . . . . . . . . . 26 2.2.1.2 發射參數轉換初始聲場 . . . . . . . . . . . . . . . . 27 2.2.1.3 密度與聲速場的三維張量建構 . . . . . . . . . . . . 29 2.2.2 訓練資料蒐集與處理 . . . . . . . . . . . . . . . . . . . . . . . . 30 2.2.3 模型架構設計與超參數設定 . . . . . . . . . . . . . . . . . . . . 32 2.3 區塊式模擬（Patch-Based Simulation） . . . . . . . . . . . . . . . . 37 2.3.1 訓練時的區塊劃分處理 . . . . . . . . . . . . . . . . . . . . . . . 37 2.3.2 推論時的區塊劃分與拼接處理 . . . . . . . . . . . . . . . . . . . 39 2.4 時序預測誤差抑制策略 . . . . . . . . . . . . . . . . . . . . . . . . . 41 2.4.1 資料增強（Data Augmentation） . . . . . . . . . . . . . . . . . . 41 2.4.2 多步預測（Multi-step Prediction） . . . . . . . . . . . . . . . . . 43 2.4.3 後處理模型（Refinement Model） . . . . . . . . . . . . . . . . . 44 2.5 模型預測結果之後處理與應用 . . . . . . . . . . . . . . . . . . . . . 45 2.5.1 穩態聲場重建 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 2.5.2 模擬感測器訊號的時間序列擷取與校正 . . . . . . . . . . . . . . 46 2.6 Omniverse 數位孿生模擬平台整合 . . . . . . . . . . . . . . . . . . . 49 2.7 實驗設備與規劃 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 2.7.1 實驗設備與運算資源規格 . . . . . . . . . . . . . . . . . . . . . . 50 2.7.2 實驗規劃 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 第三章 實驗設置與結果 55 3.1 實驗目的 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 3.1.1 模擬情境與場域說明 . . . . . . . . . . . . . . . . . . . . . . . . 55 3.1.2 模型評估與誤差計算方法 . . . . . . . . . . . . . . . . . . . . . . 57 3.2 模型架構參數影響與最佳化 . . . . . . . . . . . . . . . . . . . . . . 59 3.2.1 潛在特徵數量（Latent Features）的影響 . . . . . . . . . . . . . . 59 3.2.2 傅立葉層數（Fourier Layers）的影響 . . . . . . . . . . . . . . . 61 3.2.3 保留模態數（Retained Modes）的影響 . . . . . . . . . . . . . . 62 3.3 模型輸入與輸出聲場數量的影響 . . . . . . . . . . . . . . . . . . . . 64 3.3.1 輸入聲場數量的影響 . . . . . . . . . . . . . . . . . . . . . . . . 64 3.3.2 輸出聲場數量的影響 . . . . . . . . . . . . . . . . . . . . . . . . 65 3.4 區塊模擬方法的效能評估 . . . . . . . . . . . . . . . . . . . . . . . . 68 3.4.1 區塊化與非區塊化方法的比較 . . . . . . . . . . . . . . . . . . . 68 3.4.2 高斯函數參數的影響 . . . . . . . . . . . . . . . . . . . . . . . . 70 3.5 誤差抑制策略的效果分析 . . . . . . . . . . . . . . . . . . . . . . . . 72 3.5.1 資料增強的效果 . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 3.5.2 後處理模型的效果 . . . . . . . . . . . . . . . . . . . . . . . . . . 74 3.6 三維場模型結果分析 . . . . . . . . . . . . . . . . . . . . . . . . . . 75 3.6.1 三維均質場結果分析 . . . . . . . . . . . . . . . . . . . . . . . . 76 3.6.2 三維非均質場結果分析 . . . . . . . . . . . . . . . . . . . . . . . 80 3.7 穩態聲場轉換結果 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 3.7.1 聚焦聲場（Focus field） . . . . . . . . . . . . . . . . . . . . . . . 83 3.7.2 雙陷阱聲場（Twin-trap field）與旋渦場（Vortex field） . . . . . 86 3.8 感測器訊號轉換結果 . . . . . . . . . . . . . . . . . . . . . . . . . . 88 3.8.1 無反射物 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 3.8.2 鋁製鐵板 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 3.8.3 木頭方塊 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 3.8.4 保麗龍圓柱體 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 3.8.5 鋁製圓柱體 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 3.9 Omniverse 互動模擬介面原型 . . . . . . . . . . . . . . . . . . . . . . 96 第四章 結論與未來展望 100 4.1 結論 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 4.2 未來展望 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 參考文獻 103 附錄 A — 實作演示影片 110 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 空氣超音波陣列 | - |
| dc.subject | 數位孿生 | - |
| dc.subject | 深度學習 | - |
| dc.subject | 物理引導神經算子 | - |
| dc.subject | 超音波波動模擬 | - |
| dc.subject | 三維聲場預測 | - |
| dc.subject | Airborne Ultrasound Array | - |
| dc.subject | Digital Twin | - |
| dc.subject | Deep Learning | - |
| dc.subject | Physics-Informed Neural Operator (PINO) | - |
| dc.subject | Ultrasound Wave Simulation | - |
| dc.subject | 3D Acoustic Field Prediction | - |
| dc.title | 基於物理引導神經運算子之三維空氣超音波預測與數位孿生建構 | zh_TW |
| dc.title | 3D Airborne Ultrasound Wave Prediction and Digital Twin Construction via Physics-Informed Neural Operators | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 114-1 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 龔存雄;沈哲州 | zh_TW |
| dc.contributor.oralexamcommittee | Cihun-Siyong Gong;Che-Chou Shen | en |
| dc.subject.keyword | 空氣超音波陣列,數位孿生深度學習物理引導神經算子超音波波動模擬三維聲場預測 | zh_TW |
| dc.subject.keyword | Airborne Ultrasound Array,Digital TwinDeep LearningPhysics-Informed Neural Operator (PINO)Ultrasound Wave Simulation3D Acoustic Field Prediction | en |
| dc.relation.page | 110 | - |
| dc.identifier.doi | 10.6342/NTU202504525 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2025-10-17 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 電機工程學系 | - |
| dc.date.embargo-lift | N/A | - |
| 顯示於系所單位： | 電機工程學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-114-1.pdf 未授權公開取用 | 62.26 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。