Umair Bin Waheed
Umair Bin Waheed is an Assistant Professor of Geophysics at King Fahd University of Petroleum and Minerals. He was a postdoc at the Department of Geosciences, Princeton University, and during this time he also worked as a Writing in Science and Engineering Fellow at the Princeton Writing Program. As part of this fellowship program, he attended a course on scientific writing taught by Dr. Judith Swan and participated in several training workshops on teaching scientific writing. Later he taught courses on scientific writing to graduate students at Princeton University from across science and engineering disciplines. Encouraged by the positive feedback, he has continued to teach the material at other institutions. He has delivered courses to graduate students and postdocs at King Abdullah University of Science and Technology and University of Ontario Institute of Technology, in addition to his current institution, King Fahd University of Petroleum and Minerals.
2023 SEG Middle East Honorary Lecturer
Scientific machine learning in geophysical exploration and monitoring
Artificial intelligence, and in particular its subdomain machine learning, has revolutionized many science and engineering disciplines during the past decade. Scientific machine learning (SciML) — often referred to as scientific computing with machine learning — is an emerging interdisciplinary field that integrates traditional scientific computing methods with modern machine learning techniques. The aim of SciML is to augment data-driven learning in scientific applications where traditional machine learning approaches might struggle. Conventional machine learning models typically learn patterns from large quantities of data but may struggle with limited or noisy data sets, or where interpretability, reliability, and robustness are essential. They also often lack the ability to incorporate prior scientific knowledge, and sometimes produce results that, while statistically valid, may be physically impossible. SciML, on the other hand, combines physical models (based on scientific laws and principles) with machine learning techniques. This integration allows the models to effectively learn from smaller or noisier data sets and ensure that the outcomes are consistent with established scientific knowledge. It also offers improved interpretability and generalization capabilities.
Applications of SciML are increasingly being found in a variety of fields such as geophysics, climatology, materials science, biology, and fluid dynamics. A number of advancements have been made in recent years in the domain of geophysical exploration and monitoring using emerging SciML paradigms, including physics-informed neural networks (PINNs), Fourier neural operators (FNOs), and Deep Operator Networks (DeepONets). These developments offer a new pathway to address longstanding computational challenges in the field of geophysics. This lecture will delve into these strides forward, highlighting the potential impact of such methods and the associated challenges in making these methods mainstream.
A recording of the lecture is available.