Machine Learning in Geomechanics 2 E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: ISTE Consignment
- Sprache: Englisch
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.
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Seitenzahl: 347
Veröffentlichungsjahr: 2024
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Table of Contents
Cover
Table of Contents
Title Page
Copyright Page
Preface
Chapter 1. Data-Driven Modeling in Geomechanics
1.1. Introduction
1.2. Data-driven computational mechanics
1.3. Applications
1.4. Conclusions
1.5. References
Chapter 2. Bayesian Inference in Geomechanics
2.1. Introduction
2.2. Inverse problems
2.3. Machine learning-assisted Bayesian inference
2.4. Conclusion
2.5. References
Chapter 3. Physics-Informed and Thermodynamics-Based Neural Networks
3.1. Introduction
3.2. Physics-informed neural networks
3.3. Thermodynamics-based neural networks
3.4. Conclusions
3.5. Acknowledgments
3.6. References
Chapter 4. Introduction to Reinforcement Learning with Applications in Geomechanics
4.1. Introduction
4.2. Reinforcement learning: the basics
4.3. Applications to geomechanics
4.4. Conclusions
4.5. Acknowledgment
4.6. References
Chapter 5. Artificial Neural Networks: Basic Architectures and Training Strategies
5.1. Neural networks
5.2. Automatic differentiation
5.3. References
List of Authors
Index
Summary of Volume 1
End User License Agreement
List of Tables
Chapter 2
Table 2.1. Comparison of HMC and MFHMC algorithms for the channelized flow...
Chapter 4
Table 4.1. Comparison between the values of the state value function V...
Table 4.2. Values of the state value function for the different iteratio...
Table 4.3. Markov decision process (MDP) transition matrix. This...
Table 4.4. Value of a state-action pair (Q-values) for three differ...
Table 4.5. Mechanical and frictional properties adopted for the...
Table 4.6. Diffusion and seismicity rate system parameters
Guide
Cover
Table of Contents
Title Page
Copyright Page
Preface
Begin Reading
List of Authors
Index
Summary of Volume 1
End User License Agreement
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SCIENCES
Mechanics, Field Director – Gilles Pijaudier-Cabot
Geomechanics, Subject Head – Gioacchino Viggiani
Machine Learning in Geomechanics 2
Data-Driven Modeling, Bayesian Inference, Physics- and Thermodynamics-based Artificial Neural Networks and Reinforcement Learning
Coordinated by
Ioannis Stefanou
Félix Darve
First published 2024 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.
Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address:
ISTE Ltd27-37 St George’s RoadLondon SW19 4EUUK
www.iste.co.uk
John Wiley & Sons, Inc.111 River StreetHoboken, NJ 07030USA
www.wiley.com
© ISTE Ltd 2024The rights of Ioannis Stefanou and Félix Darve to be identified as the authors of this work have been asserted by them in accordance with the Copyright, Designs and Patents Act 1988.
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s), contributor(s) or editor(s) and do not necessarily reflect the views of ISTE Group.
Library of Congress Control Number: 2024943342
British Library Cataloguing-in-Publication DataA CIP record for this book is available from the British LibraryISBN 978-1-78945-193-1
ERC code:PE6 Computer Science and InformaticsPE6_7 Artificial intelligence, intelligent systems, natural language processingPE10 Earth System SciencePE10_19 Planetary geology and geophysics
Preface
Ioannis STEFANOU1 and Félix DARVE2
1 GeM, UMR 6183, CNRS, Ecole Centrale Nantes, Nantes Université, France
23SR Laboratory, CNRS, Grenoble INP – ENSE3, Université Grenoble Alpes, France
When discussing artificial intelligence (AI), some basic questions immediately emerge: What is AI? How does it work?
Behind this well-known term, a collection of methods of applied mathematics allow the computer to learn and identify patterns in data. This collection of methods is called machine learning (ML) and it is the target of these two volumes, which were authored for the 2023 ALERT Geomaterials Doctoral School.
In combination with the tremendous increase in the computational power, ML has led to incredible achievements in many disciplines of science and technology. These achievements were that striking that some researchers believe that ML could become a turning point for humanity, as the discovery of fire was for our far ancestors!
Until the 1960s, the scientific development has been characterized by the so-called “linear physics” and by modeling represented by analytical equations solved explicitly by the available mathematical tools giving rise to analytical solutions. The field of problems that can be solved in this way is, of course, precious, but very limited.
Then the numerical revolution, based on powerful numerical methods and computers, allowed to solve numerically a large variety of problems, which can be described by a known system of equations. Many limits of this methodology are known due to the abundance of nonlinear processes in nature, chaos and complexity. In any case, numerical analysis, another branch of applied mathematics, has immensely enlarged the class of problems that can be solved today.
However, the numerical solutions of these sets of nonlinear equations can be computationally very intensive or even impossible. Moreover, many problems in engineering are difficult to describe by a set of equations. ML tools provide promising methods for addressing both those problems.
Another aspect of ML algorithms is their ability to solve very complex problems in a “creative” manner. One characteristic example of creativity was shown when the machine beat the world champion of Go, which was invented a long time ago in China. Differently from chess, in which the computer can predict the game evolution several moves in advance, in Go the number of possible moves is extremely large (higher than the number of atoms in the known universe). Therefore, it is necessary to follow creative strategies. Indeed, the machine, “AlphaGo”, has shown that it is able to carry out novel strategies that surprised even the best human players in the world.
All these methods of ML give new powerful tools to scientists and engineers and open new perspectives in geomechanics. The target of these two volumes is to demystify ML to present its main methods and to show some examples of applications in mechanics and geomechanics. Most of the chapters of the volume were edited to provide a pedagogical introduction to the most important methods of ML and to uncover the fundamental notions behind them.
Volume I contains the following five chapters:
The first chapter, “Overview of machine learning in geomechanics”, is the introductory chapter of this volume. In this chapter, we explain how the machine can learn, show a classification of the main methods in ML, outline some applications of ML in geomechanics and highlight its limitations.
The second chapter, “Introduction to regression methods”, focuses on regression, which is one of the fundamental pillars of supervised ML. In this chapter, we introduce the essential concepts in regression analysis and methods by providing hands-on, practical examples.
The target of the third chapter, “Unsupervised learning: Basic concepts and application to particle dynamics”, is twofold. The first part of this chapter is devoted to the description of the basic concepts of the most popular techniques of unsupervised learning. The second part illustrates an application of unsupervised learning to the discovery of patterns in particles dynamics.
The fourth chapter, “Classification techniques in machine learning”, aims to describe what the problem of classification in ML is and illustrates some of the methods used for solving it, without resorting to artificial neural networks (ANNs). Hands-on examples are given and active learning is discussed.
The fifth chapter, “Artificial neural networks: Learning the optimum statistical model from data”, provides a comprehensive introduction to ANNs. Several hands-on examples are given to help the reader grasp the main ideas.
Volume II is organized as follows:
The first chapter, “Data-driven modeling in geomechanics”, presents the theoretical framework of the so-called data-driven computational mechanics. Furthermore, it shows some of its applications for the solution of problems involving Cauchy and Cosserat continua with elastic and inelastic materials, which, naturally, represent common descriptions of geomaterials.
The second chapter, “Bayesian inference in geomechanics”, is intended to provide a concise exploration of Bayesian inference and demonstrates how recent advancements in ML can assist in efficient Bayesian inference within the realm of geomechanics applications.
The third chapter, “Physics-informed and thermodynamics-based neural networks”, shows how to inject prior knowledge into deep learning algorithms. Using various examples, we present physics-informed neural networks for the discovery of partial differential equations and thermodynamics-based ANNs for the discovery of constitutive models of complex, inelastic materials.
The fourth chapter, “Introduction to reinforcement learning with applications in geomechanics”, presents the basic concepts of reinforcement learning, which enables the development of software agents that are capable of making optimal decisions in dynamic and uncertain environments. The chapter closes with two applications of reinforcement learning in geomechanics.
The fifth chapter, “Artificial neural networks: basic architectures and training strategies”, presents more architectures of ANNs and discusses training strategies.
We deeply thank all the authors of the volumes for their comprehensive contributions and their effort to present complex notions in a pedagogical manner. We also deeply thank ALERT Geomaterials for the organization of this doctoral school and all students for their active participation. We hope that the chapters provide a valuable introduction to machine learning in geomechanics.
Ioannis STEFANOU
Félix DARVE
June 2024
