Deep Learning in Computational Mechanics: An Introductory Course - Leon Herrmann

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• Brand: Leon Herrmann
• Categoria: Computers
• Magazin: libris.ro
• Ultima actualizare: 16-06-2026 00:26:07

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Descriere magazin:
Leon Herrmann has a uniquely diverse background; born in South Africa and ngrowing up in seven different countries. He earned a bachelor\'s degree in nMechanical Engineering from the Technical University of Denmark (DTU) and a nmaster\'s degree in Computational Mechanics from the Technical University of nMunich (TUM), where he also obtained his doctorate for his work in ncomputational mechanics with neural networks. His primary research focus has nbeen on finite element methods, fracture in composite materials, and combining ntraditional numerical simulations with modern machine learning techniques. As a product of the Mauerfall, Moritz Jokeit grew up in the non-existing town of nBielefeld and the alpine foothills near Rosenheim. Following his bachelor\'s ndegree in Civil Engineering, he studied Computational Mechanics at the nTechnical University of Munich (TUM) and the Polytechnic University of Catalonia n(UPC). His passion for deep learning and computational mechanics was ntransformed into a master thesis that laid the groundwork for this lecture book. nAfter his graduation he continued his research at the Chair of Computational nModeling and Simulation. He is now a doctoral candidate at the Institute for nBiomechanics at the ETH Zürich focusing on the mechanics of the spine. Oliver Weeger is a Full Professor for Cyber-Physical Simulation with the nDepartment of Mechanical Engineering at the Technical University of Darmstadt nin Germany. He graduated in Techno-Mathematics from TU Munich in 2011 and nobtained his Ph.D. in Mathematics from TU Kaiserslautern in 2015. Before joining nTU Darmstadt in 2019, he had been working at the Singapore University of nTechnology and Design as a Postdoctoral Researcher and Assistant Professor. nHis passion for research and education evolves around advanced computational nmethods, modeling, and optimization approaches for nonlinear, multiscale, and nmultiphysics problems in engineering. In particular, this includes the fusion of nmachine learning, classical modeling, and simulation to obtain flexible and yet naccurate, reliable and robust predictive models for computational mechanics. Stefan Kollmannsberger graduated in Civil Engineering in 1998 and worked for nseveral years as heavy underground construction engineer before returning to nuniversity to devote himself to computational mechanics. He graduated with a nPhD at the Technical University of Munich in 2009, where he enjoyed leading the nresearch group Simulation in Applied Mechanics until 2023. Since then, he is nfull professor at the Bauhaus University in the culturally opulent city of Weimar nand heads the Chair of Data Science in Construction. He is dedicated to both nteaching and science and uses the content of this lecture book as a basis for an nintroductory course in the field of artificial intelligence in computational nmechanics.