A Practical Guide to Machine Learning in Materials Science and Engineering
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A Practical Guide to Machine Learning in Materials Science and Engineering

A Practical Guide to Machine Learning in Materials Science and Engineering


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About the Book

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.

Table of Contents:
What is Artificial Intelligence Artificial Intelligence in Material Science and Engineering PART 1: MACHINE LEARNING Problems in Machine Learning ; Machine Learning Techniques Classical Machine Learning Feature Extraction Deep Learning Training Machine Learning Models Fitting Data Handling Transfer Learning PART 2: MACHINE LEARNING APPLICATIONS IN MATERIALS SCIENCE AND ENGINEERING Microstructure Surfaces Fracture surfaces Tomographic methods Anomaly detection Process-microstructure-property correlations PART 3: MACHINE LEARNING USING IN-VIVO DATA Material Data Repositories Training Data and Annotations Segmentation and Annotation Manual Segmentation Ground Truth using Correlative Microscopy PART 4: MACHINE LEARNING USING SYNTHETIC DATA The Digital Reality Cycle, Deep vs. Shallow Statistical Modelling of Microstructures Monte-Carlo Simulation of Electron Backscatter Imaging Randomizing Image Textures Generative Adversarial Networks: Pros and Cons

About the Author :
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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Product Details
  • ISBN-13: 9783527353149
  • Publisher: Wiley-VCH Verlag GmbH
  • Publisher Imprint: Wiley-VCH Verlag GmbH
  • Height: 244 mm
  • No of Pages: 275
  • Returnable: N
  • ISBN-10: 3527353143
  • Publisher Date: 14 Feb 2025
  • Binding: Paperback
  • Language: English
  • Returnable: N
  • Width: 170 mm


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