Introduction
The rapid evolution of machine learning technologies has revolutionized numerous fields, including geophysics, by offering advanced solutions to complex problems that were previously intractable. Geophysical modeling and inversion, critical to the exploration of Earth’s subsurface, have significantly benefited from these advancements. The core objective of this course is to explain basic theories of scientific machine learning (SciML) and equip participants with skills in implementing these tools to solve partial differential equations (PDEs) and the associated inverse problems, with a particular focus on eikonal and wave equations.
Duration
8 hours
CEUs
.8
Intended Audience
The course is targeted towards computational geophysicists who have some familiarity with neural networks and programming in Python.
Prerequisites
None
Learner Outcomes
Participants will gain a solid theoretical foundation in SciML concepts and learn how to apply these techniques to geophysical modeling and inversion, with a focus on solving partial differential equations (PDEs) such as the eikonal and wave equations. Additionally, the course will provide insights into emerging trends and future research directions, preparing participants to contribute to advancements in geophysical modeling and inversion using SciML.
Course Outline
- Introduction to PINNs
- PINNs for solving forward and inverse problems in Geophysics
- Introduction to Neural Operators
- Solution of forward and inverse problems using neural operators
- Physics-informed Neural Operators
- Emerging trends in Scientific Machine Learning
Instructor Bio
Umair bin Waheed
Assistant Professor of Geophysics, King Fahd University of Petroleum and Minerals
View Bio
