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課程名稱 |
機器學習應用於偏微分方程
Machine Learning for PDEs |
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開課學期 |
110-2 |
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授課對象 |
國家理論科學研究中心 |
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授課教師 |
薛名成 |
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課號 |
NCTS5051 |
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課程識別碼 |
V41 U5090 |
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班次 |
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學分 |
1.0 |
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全/半年 |
半年 |
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必/選修 |
選修 |
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上課時間 |
第1,2,3,4,5,6 週
星期五7,8,9(14:20~17:20) |
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上課地點 |
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備註 |
密集課程。上課日期：第一~六週，週五
線上授課。
聯絡：游墨霏murphyyu@ncts.tw與胡偉帆合授
總人數上限：40人
外系人數限制：15人 |
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課程簡介影片 |
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核心能力關聯 |
本課程尚未建立核心能力關連 |
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課程大綱
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課程概述 |
1. Traditional numerical methods for solving differential equations (Prof. Ming-Cheng Shiue)
2. Mathematical model for neural networks (Prof. Wei-Fan Hu)
3. Neural network approximations for solving PDEs (Prof. Wei-Fan Hu) |
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課程目標 |
Nowadays, deep learning has achieved great success in various scientific disciplines, including image recognition, natural language processing, and many other practical applications in our daily life. When it comes to solving partial differential equations (PDEs), traditional numerical methods are well developed tools for finding solutions accurately, whereas sometimes tackling problems with complicated setup or in high dimensions using traditional methods can be extremely computational expensive or even infeasible. In this scenario deep learning techniques come to play an essential role to overcome those difficulties. In this lecture, we aim to introduce some traditional numerical methods for PDEs, then carry out mathematical model of neural networks and apply machine learning techniques to solve PDEs. |
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課程要求 |
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預期每週課後學習時數 |
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Office Hours |
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指定閱讀 |
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參考書目 |
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評量方式
(僅供參考) |
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