Machine Learning in Geomechanics

This course is meant as a brief introduction to machine learning as it applies to geomechanics. We will start with an introduction to continuum and distinct-element numerical modeling. Then machine learning and surrogate modeling will be introduced and explained. We will discuss the advantages of each methodology and highlight the capability of numerical simulations to empower machine learning. Then we will present two real-world projects that involved the use of machine learning.

Course curriculum

    1. Introduction

    1. Introduction

    2. Why Discuss Numerical Modeling?

    3. What is Numerical Modeling?

    4. Numerical Modeling Methods & Software

    5. Explicit and Implicit Methods

    6. Continuum Modeling Advantages and Limitations

    7. Discontinuum Modeling Advantages and Limitations

    8. When To Use Numerical Models

    9. Model Simplification

    10. Model Size and Boundaries

    11. Workflow for Numerical Analysis

    12. Final Thoughts

    1. Introduction

    2. What is Machine Learning?

    3. Machine Learning Framework

    4. Feature Encoding & Engineering

    5. Model Selection & Training

    6. Model Evaluation & Tuning

    7. Utilizing Numerical Models & Machine Learning

    8. Data Generation & Sampling

    9. How to Get Started

    10. Final Thoughts

    1. Introduction

    2. Background

    3. Project Motivation

    4. Data & Preprocessing

    5. Machine Learning Model

    6. Analysis

    7. Conclusions

    1. Introduction

    2. Background

    3. Why Build a Surrogate Model?

    4. Numerical Model Development

    5. Design of Experiments

    6. Training A Surrogate Model

    7. Informing the Discontinuum Model

    8. Conclusion

    1. Feedback

About this course

  • Free
  • 39 lessons
  • 2 hours of video content

Instructors

Social proof: reviews