Steven L. Brunton and J. Nathan Kutz, Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control, 2nd edition, Cambridge University Press (pdf, amazing videos, connection with dynamical systems in engineering)
10/01/2025
12/30/2024
Practical Engineering
Practical Engineering is all about infrastructure and the human-made world around us. It is hosted, written, and produced by civil engineer, Grady Hillhouse. We have new videos posted every first and third Tuesday, so please subscribe for updates.
10/10/2024
8/20/2024
4/01/2024
1 兆電晶體 GPU 的到來
鉅亨網新聞中心，台積電董事長劉德音撰文 談1兆電晶體GPU的到來，2024-03-29
文中指出，從 1997 年擊敗西洋棋人類冠軍的「深藍」，到 2023 年爆火的 ChatGPT，再過 15 年，人工智慧已經發展到可以「合成知識」(synthesize knowledge) 的地步，可以創作詩歌、創作藝術品、診斷疾病、編寫總結報告和電腦程式碼，甚至可以設計與人類製造的積體電路相媲美的積體電路。
12/03/2023
Machine-Guided Discovery of a Real-World Rogue Wave (瘋狗浪) Model
台北天文館，AI 找到如何預測巨浪的公式，2023 年 12 月 02 日
Dion Häfner, Johannes Gemmrich, Markus Jochum, Machine-Guided Discovery of a Real-World Rogue Wave Model, Proceedings of the National Academy of Sciences (2023), 120(48). (arXiv, data, code)
4/14/2023
Using AI to Accelerate Scientific Discovery
Demis Hassabis, Using AI to Accelerate Scientific Discovery, Institute for Ethics in AI Oxford, 2022.
3/05/2022
3/04/2022
讓 AI 幫你最佳化太陽能電池材料的製程參數
採訪撰文 簡克志，美術設計 林洵安，機器學習 x 鈣鈦礦材料：讓 AI 幫你最佳化太陽能電池材料的製程參數！，研之有物，2022-02-21
機器學習輔助材料設計
為了 2050 淨零排放的目標，太陽能發電為不可或缺的再生能源之一，其中「鈣鈦礦太陽能電池」是近年最熱門的研究領域，不僅成本低廉、光電轉換效率也可達到 25%。然而，鈣鈦礦材料在環境中容易降解，影響使用壽命。材料科學家為了做出效能好又穩定的鈣鈦礦「料理」，無不卯足了勁，替這道菜加上各種「食材」，但是越複雜的菜，調出好味道就越困難。人腦畢竟有限，如果交給機器呢？中央研究院「研之有物」專訪院內應用科學研究中心包淳偉研究員，他與團隊訓練了一套機器學習模型，可以又快又準的找出複雜鈣鈦礦材料的最佳化條件！
11/06/2021
47-779 Quantum Integer Programming
Prof. Sridhar Tayur, Dr. Davide Venturelli, and David Bernal, 47-779 Quantum Integer Programming (QuIP), Fall 2020. (lecture note)
This course is primarily designed for graduate students (and advanced undergraduates) across CMU campuses interested in integer programming (with non-linear objective functions) and the potential of near-term quantum computing for solving combinatorial optimization problems. By the end of the semester, someone enrolled in this course should be able to:
- Identify the current status of quantum computing and its potential uses for integer programming
- Access and use quantum computing resources (such as DWave Quantum Annealers)
- Set up a given integer program to be solved with quantum computing
- Work in groups collaboratively on a state-of-the-art project regarding applications of quantum computing and integer programming
11/05/2021
9/26/2021
科技部半導體射月計畫 109 年度產學技術交流
科技部半導體射月計畫 109 年度產學技術交流
- Boris Murmann, tinyML: The Perfect Storm for Innovation in Ultra-Low-Power System Design
- 梁伯嵩，IC 運算平台趨勢: 數位運算、人工智慧與量子運算
- Tetsu Ohtou, Semiconductor Process and Equipment Technology for Advanced Logic Devices
- 陳俊雄，汽車產業及感測元件發展趨勢
9/24/2021
Quantum Computers, Explained With Quantum Physics
Quanta Magazine, Quantum Computers, Explained With Quantum Physics, 2021/6/8
9/06/2021
A high-bias, low-variance introduction to Machine Learning for physicists
Pankaj Mehta, Marin Bukov, Ching-Hao Wang, Alexandre G.R. Day, Clint Richardson, Charles K. Fisher, David J. Schwab, A high-bias, low-variance introduction to Machine Learning for physicists, Phyics Reports, 810 (2019) 1-124. (Python, Github)
Machine Learning (ML) is one of the most exciting and dynamic areas of modern research and application. The purpose of this review is to provide an introduction to the core concepts and tools of machine learning in a manner easily understood and intuitive to physicists. The review begins by covering fundamental concepts in ML and modern statistics such as the bias-variance tradeoff, overfitting, regularization, generalization, and gradient descent before moving on to more advanced topics in both supervised and unsupervised learning. Topics covered in the review include ensemble models, deep learning and neural networks, clustering and data visualization, energy-based models (including MaxEnt models and Restricted Boltzmann Machines), and variational methods. Throughout, we emphasize the many natural connections between ML and statistical physics. A notable aspect of the review is the use of Python Jupyter notebooks to introduce modern ML/statistical packages to readers using physics-inspired datasets (the Ising Model and Monte-Carlo simulations of supersymmetric decays of proton-proton collisions). We conclude with an extended outlook discussing possible uses of machine learning for furthering our understanding of the physical world as well as open problems in ML where physicists may be able to contribute.
11/27/2020
專題實作和金門行銷
這學期上一門大三的專案實作，今天談到以考試為主的傳統教學缺點，我舉兩個例子說明。朋友的小孩去年唸小二，學校教月的陰晴圓缺，竟然是用背誦的，小朋友痛苦不堪。朋友是工程碩士，透過兩個球體和觀察，了解月形狀的變化，輕輕鬆鬆就記下來了。令人驚訝的不是背不下來的小孩，而是班上九成的同學硬背下來，這才是教育的根本問題。更深層的原因是我們的師資培育方式，我們現在是包班制，這是制度造成的問題；數理比較抽象，在芬蘭需要具備有(理工) 碩士才能任教。
9/03/2020
8/19/2020
Rob Smedley From Formula 1 Talks About Using AWS to Improve the Fan Experience
AWS re:Invent 2019 – Rob Smedley From Formula 1 Talks About Using AWS to Improve the Fan Experience, 2019/12/4
Formula 1 has been using Amazon EC2 for Computational Fluid Dynamics (CFD) to simulate race car aerodynamics, achieving the performance of a super computer at a much lower cost and reducing simulation time by an average of 70% — from 60 hours down to 18 hours. With the CFD project, Formula 1 used over 500 million data points to study downforce loss when two vehicles race in close proximity. (A car’s downforce increases its tire grip and cornering speed and reduces lap time.) Based on its CFD simulations, Formula 1 has designed a car for the 2021 racing season that reduces downforce loss in wheel-to-wheel racing from 50% to 15% — and offers a more exciting experience for fans.
8/01/2020
崴昊科技的工程最佳化軟體
李家同，為台灣加油打氣專欄 (213) 國產工程最佳化軟體，2020 年 7 月 29 日
這次要介紹的是一種台灣自行開發的工程最佳化軟體，這種軟體是 CAE (Computer Aided Engineering) 軟體的一種。要設計一個工業產品，通常都要有一個模擬軟體(simulation software)，也就是說，我們要測試一下所設計的產品能否使用。比方說，我們設計了一個馬達，當然要測試這個馬達能不能轉，這可以用模擬軟體來測驗。如果我們設計了一個電子電路，要知道這個電子電路是否符合要求，也可以用模擬軟體來測驗。
8/14/2015
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