Practical Guide to Machine Learning in Materials Science and Engineering

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Product details

  • ISBN 9783527353149
  • Dimensions: 170 x 244mm
  • Publication Date: 14 Feb 2025
  • Publisher: Wiley-VCH Verlag GmbH
  • Publication City/Country: DE
  • Product Form: Paperback
  • Language: English
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Indispensable resource with tried and tested approaches for everyone who is faced with analyzing huge amounts of data in materials science and engineering using machine learning techniques.
Tim Dahmen, PhD, is senior researcher at the German Research Centre for Artificial Intelligence (DFKI) where he heads the team Computational 3D Imaging. His research interests are the application of Artificial Intelligence in material science and engineering.

Frank Mücklich, PhD, is Professor for Functional Materials at the University of Saarland, Germany. After his PhD he became head of the working group on metallography at the TU Bergakademie Freiberg. In 1990 he began working at the Max Planck Institute for Metal Research in Stuttgart before accepting the professorship in Saarland in 1995. Frank Mücklich founded the European School for Materials Research in 2008 and the Material Engineering Center Saarland in 2009.

Martin Müller is researcher at the University of Saarland in the Material Engineering Center Saarland, Germany. He did his MSc research at the Dillinger Hüttenwerke and worked as material engineer at Brück GmbH before starting his PhD work at the University of Saarland.

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