Deep Learning in Computational Mechanics: An Introductory Course
暫譯: 計算力學中的深度學習：入門課程

本書提供了一門不需要先備知識的入門課程。首先介紹機器學習的基本概念，然後再解釋神經網絡。在掌握這些知識後，將探討深度學習在模擬中的重要主題，包括代理建模、物理知識驅動的神經網絡、生成式人工智慧、哈密頓/拉格朗日神經網絡、輸入凸神經網絡，以及更一般的機器學習技術。

本書的理念是以數學上合理的方式提供盡可能簡單的基本概念。起始點是包括彈性、塑性、熱演化或波動傳播的一維範例。然後將這些概念擴展到材料建模、生成式人工智慧、拓撲優化、缺陷檢測和反問題等最先進的應用。

Leon Herrmann has a uniquely diverse background; born in South Africa and
growing up in seven different countries. He earned a bachelor's degree in
Mechanical Engineering from the Technical University of Denmark (DTU) and a
master's degree in Computational Mechanics from the Technical University of
Munich (TUM), where he also obtained his doctorate for his work in
computational mechanics with neural networks. His primary research focus has
been on finite element methods, fracture in composite materials, and combining
traditional numerical simulations with modern machine learning techniques.

As a product of the "Mauerfall", Moritz Jokeit grew up in the non-existing town of
Bielefeld and the alpine foothills near Rosenheim. Following his bachelor's
degree in Civil Engineering, he studied Computational Mechanics at the
Technical University of Munich (TUM) and the Polytechnic University of Catalonia
(UPC). His passion for deep learning and computational mechanics was
transformed into a master thesis that laid the groundwork for this lecture book.
After his graduation he continued his research at the Chair of Computational
Modeling and Simulation. He is now a doctoral candidate at the Institute for
Biomechanics at the ETH Zürich focusing on the mechanics of the spine.

Oliver Weeger is a Full Professor for Cyber-Physical Simulation with the
Department of Mechanical Engineering at the Technical University of Darmstadt
in Germany. He graduated in Techno-Mathematics from TU Munich in 2011 and
obtained his Ph.D. in Mathematics from TU Kaiserslautern in 2015. Before joining
TU Darmstadt in 2019, he had been working at the Singapore University of
Technology and Design as a Postdoctoral Researcher and Assistant Professor.
His passion for research and education evolves around advanced computational
methods, modeling, and optimization approaches for nonlinear, multiscale, and
multiphysics problems in engineering. In particular, this includes the fusion of
machine learning, classical modeling, and simulation to obtain flexible and yet
accurate, reliable and robust predictive models for computational mechanics.

Stefan Kollmannsberger graduated in Civil Engineering in 1998 and worked for
several years as heavy underground construction engineer before returning to
university to devote himself to computational mechanics. He graduated with a
PhD at the Technical University of Munich in 2009, where he enjoyed leading the
research group "Simulation in Applied Mechanics" until 2023. Since then, he is
full professor at the Bauhaus University in the culturally opulent city of Weimar
and heads the Chair of Data Science in Construction. He is dedicated to both
teaching and science and uses the content of this lecture book as a basis for an
introductory course in the field of artificial intelligence in computational
mechanics.

Leon Herrmann 擁有獨特而多元的背景；他出生於南非，並在七個不同的國家成長。他在丹麥技術大學 (DTU) 獲得機械工程學士學位，並在慕尼黑工業大學 (TUM) 獲得計算力學碩士學位，並因其在計算力學與神經網絡方面的研究獲得博士學位。他的主要研究重點是有限元素法、複合材料的斷裂，以及將傳統數值模擬與現代機器學習技術相結合。

作為「圍牆倒塌」的產物，Moritz Jokeit 在不存在的比勒費爾德市和羅森海姆附近的阿爾卑斯山腳下成長。在獲得土木工程學士學位後，他在慕尼黑工業大學 (TUM) 和加泰羅尼亞理工大學 (UPC) 學習計算力學。他對深度學習和計算力學的熱情轉化為一篇碩士論文，為這本講義書奠定了基礎。畢業後，他在計算建模與模擬的教席繼續進行研究。現在，他是蘇黎世聯邦理工學院 (ETH Zürich) 生物力學研究所的博士候選人，專注於脊椎的力學。

Oliver Weeger 是德國達姆施塔特技術大學機械工程系的網路物理模擬全職教授。他於2011年在慕尼黑工業大學獲得科技數學學位，並於2015年在凱瑟斯勞滕大學獲得數學博士學位。在2019年加入達姆施塔特技術大學之前，他曾在新加坡科技設計大學擔任博士後研究員和助理教授。他對研究和教育的熱情圍繞著先進的計算方法、建模和優化方法，應用於工程中的非線性、多尺度和多物理問題。特別是，這包括將機器學習、傳統建模和模擬相結合，以獲得靈活且準確、可靠和穩健的計算力學預測模型。

Stefan Kollmannsberger 於1998年獲得土木工程學位，並在重型地下建設工程師工作了幾年，然後回到大學專注於計算力學。他於2009年在慕尼黑工業大學獲得博士學位，並在2023年之前一直領導「應用力學中的模擬」研究小組。自那時起，他成為魏瑪包豪斯大學的全職教授，並負責建築數據科學教席。他致力於教學和科學，並利用這本講義書的內容作為計算力學中人工智慧領域入門課程的基礎。