Machine Learning in Geomechanics 1

Summary of Volume 2

Preface

Ioannis STEFANOU and Félix DARVE

Chapter 1. Data-Driven Modeling in Geomechanics

Konstantinos KARAPIPERIS

• 1.1 Introduction
• 1.2 Data-driven computational mechanics
  • 1.2.1 Cauchy continuum – elasticity
  • 1.2.2 Micropolar continuum – elasticity
  • 1.2.3 Extension to inelasticity
  • 1.2.4 Data sampling
• 1.3 Applications
• 1.4 Conclusions
• 1.5 References

Chapter 2. Bayesian Inference in Geomechanics

Dhruv V. PATEL, Jonghyun “Harry” LEE, Peter K. KITANIDIS and Eric F. DARVE

• 2.1 Introduction
• 2.2 Inverse problems
  • 2.2.1 Regularization methods
  • 2.2.2 Bayesian inversion
• 2.3 Machine learning-assisted Bayesian inference
  • 2.3.1 Informative and accurate prior characterization with deep generative modeling
  • 2.3.2 Computationally inexpensive likelihood evaluation with operator learning
  • 2.3.3 Efficient posterior inference in a black-box setting
• 2.4 Conclusion
• 2.5 References

Chapter 3. Physics-Informed and Thermodynamics-Based Neural Networks

Filippo MASI and Ioannis STEFANOU

• 3.1 Introduction
• 3.2 Physics-informed neural networks
  • 3.2.1 Methodology
  • 3.2.2 Hands-on example
• 3.3 Thermodynamics-based neural networks
  • 3.3.1 Theoretical framework
  • 3.3.2 Methodology
  • 3.3.3 Digital twins of granular materials: a pedagogic example
  • 3.3.4 Speed up multiscale simulations
• 3.4 Conclusions
• 3.5 Acknowledgments
• 3.6 References

Chapter 4. Introduction to Reinforcement Learning with Applications in Geomechanics

Alexandros STATHAS, Diego GUTIERRÉZ-ORIBIO and Ioannis STEFANOU

• 4.1 Introduction ...