{"product_id":"deep-learning-in-computational-mechanics-an-introductory-course-9783031895289","title":"Deep Learning in Computational Mechanics: An Introductory Course","description":"This book provides a first course without requiring prerequisite knowledge. Fundamental concepts of machine learning are introduced before explaining neural networks. With this knowledge, prominent topics in deep learning for simulation are explored. These include surrogate modeling, physics-informed neural networks, generative artificial intelligence, Hamiltonian\/Lagrangian neural networks, input convex neural networks, and more general machine learning techniques. The idea of the book is to provide basic concepts as simple as possible but in a mathematically sound manner. Starting point are one-dimensional examples including elasticity, plasticity, heat evolution, or wave propagation. The concepts are then expanded to state-of-the-art applications in material modeling, generative artificial intelligence, topology optimization, defect detection, and inverse problems.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eLeon Herrmann has a uniquely diverse background; born in South Africa and \u003cbr\u003egrowing up in seven different countries. He earned a bachelor's degree in \u003cbr\u003eMechanical Engineering from the Technical University of Denmark (DTU) and a \u003cbr\u003emaster's degree in Computational Mechanics from the Technical University of \u003cbr\u003eMunich (TUM), where he also obtained his doctorate for his work in \u003cbr\u003ecomputational mechanics with neural networks. His primary research focus has \u003cbr\u003ebeen on finite element methods, fracture in composite materials, and combining \u003cbr\u003etraditional numerical simulations with modern machine learning techniques. \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003eAs a product of the \"Mauerfall\", Moritz Jokeit grew up in the non-existing town of \u003cbr\u003eBielefeld and the alpine foothills near Rosenheim. Following his bachelor's \u003cbr\u003edegree in Civil Engineering, he studied Computational Mechanics at the \u003cbr\u003eTechnical University of Munich (TUM) and the Polytechnic University of Catalonia \u003cbr\u003e(UPC). His passion for deep learning and computational mechanics was \u003cbr\u003etransformed into a master thesis that laid the groundwork for this lecture book. \u003cbr\u003eAfter his graduation he continued his research at the Chair of Computational \u003cbr\u003eModeling and Simulation. He is now a doctoral candidate at the Institute for \u003cbr\u003eBiomechanics at the ETH Zürich focusing on the mechanics of the spine. \u003cp\u003e\u003c\/p\u003eOliver Weeger is a Full Professor for Cyber-Physical Simulation with the \u003cbr\u003eDepartment of Mechanical Engineering at the Technical University of Darmstadt \u003cbr\u003ein Germany. He graduated in Techno-Mathematics from TU Munich in 2011 and \u003cbr\u003eobtained his Ph.D. in Mathematics from TU Kaiserslautern in 2015. Before joining \u003cbr\u003eTU Darmstadt in 2019, he had been working at the Singapore University of \u003cbr\u003eTechnology and Design as a Postdoctoral Researcher and Assistant Professor. \u003cbr\u003eHis passion for research and education evolves around advanced computational \u003cbr\u003emethods, modeling, and optimization approaches for nonlinear, multiscale, and \u003cbr\u003emultiphysics problems in engineering. In particular, this includes the fusion of \u003cbr\u003emachine learning, classical modeling, and simulation to obtain flexible and yet \u003cbr\u003eaccurate, reliable and robust predictive models for computational mechanics. \u003cp\u003e\u003c\/p\u003eStefan Kollmannsberger graduated in Civil Engineering in 1998 and worked for \u003cbr\u003eseveral years as heavy underground construction engineer before returning to \u003cbr\u003euniversity to devote himself to computational mechanics. He graduated with a \u003cbr\u003ePhD at the Technical University of Munich in 2009, where he enjoyed leading the \u003cbr\u003eresearch group \"Simulation in Applied Mechanics\" until 2023. Since then, he is \u003cbr\u003efull professor at the Bauhaus University in the culturally opulent city of Weimar \u003cbr\u003eand heads the Chair of Data Science in Construction. He is dedicated to both \u003cbr\u003eteaching and science and uses the content of this lecture book as a basis for an \u003cbr\u003eintroductory course in the field of artificial intelligence in computational \u003cbr\u003emechanics. \u003cbr\u003e","brand":"Springer","offers":[{"title":"Default Title","offer_id":52160100139282,"sku":"9783031895289","price":118.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0831\/4771\/8930\/files\/img_b7b47f03-74f4-45b5-b1d7-2efbc4a49195.jpg?v=1775036017","url":"https:\/\/surprise-castle.myshopify.com\/products\/deep-learning-in-computational-mechanics-an-introductory-course-9783031895289","provider":"Surprise Castle","version":"1.0","type":"link"}