Machine Learning in Geomechanics 1: Overview of Machine Learning, Unervised Learning, Regression, Classification and Artificial Neural Networks - Couverture rigide

Couverture rigide

ISBN 10 : 1789451922 ISBN 13 : 9781789451924

Editeur : ISTE Ltd, 2025

Synopsis

Machine learning has led to incredible achievements in many different fields of science and technology. These varied methods of machine learning all offer powerful new tools to scientists and engineers and open new paths in geomechanics.

The two volumes of Machine Learning in Geomechanics aim to demystify machine learning. They present the main methods and provide examples of its applications in mechanics and geomechanics. Most of the chapters provide a pedagogical introduction to the most important methods of machine learning and uncover the fundamental notions underlying them.

Building from the simplest to the most sophisticated methods of machine learning, the books give several hands-on examples of coding to assist readers in understanding both the methods and their potential and identifying possible pitfalls.

A propos du livre

À propos de l?auteur

Ioannis Stefanou is Professor at ECN, France, and leads several geomechanics projects. His main research interests include mechanics, geomechanics, control, induced seismicity and machine learning.

Félix Darve is Emeritus Professor at the Soils Solids Structures Risks (3SR) laboratory, Grenoble-INP, Grenoble Alpes University, France. His research focuses on computational geomechanics.

Détails de l'édition

• Éditeur: ISTE Ltd
• Date d'édition: 2025
• Langue: anglais
• ISBN 10: 1789451922
• ISBN 13: 9781789451924
• Reliure: Relié
• Nombre de pages: 272
• Éditeur: Stefanou Ioannis, Darve Félix
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