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.
This one-day course will cover theoretical concepts associated with physics-informed neural networks and neural operators. Subsequently, these methods will be used to solve geophysical forward and inverse problems, followed by emerging research trends related to the topic.
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.
The course is targeted towards computational geophysicists who have some familiarity with neural networks and programming in Python.
Associate Professor of Geophysics, King Fahd University of Petroleum and Minerals (KFUPM)
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