Format: Virtual Webinar. 45 min. presentation followed by 15 min. Q&A
An optional post-lecture workshop will immediately follow each lecture for expanded Q&A and networking
Session 1, Monday, Dec 4, 2023, 10 am to 11 am US Central Time
Session 2, Monday, Jan 15, 2024 11 pm to 12 am US Central Time
Two live sessions are completed. Please scroll down to watch the videos from the recordings below. SEG members, view the course for free!
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
Umair bin Waheed is an Associate Professor of Geophysics at King Fahd University of Petroleum and Minerals (KFUPM). His research interests are broadly in the area of computational geosciences. Specifically, he is interested in automating and improving geophysical workflows through the use of smart algorithms for enhanced decision making in subsurface energy systems. Umair graduated from KAUST with a Ph.D. under the supervision of Tariq Alkhalifah in 2015. Prior to joining KFUPM in 2017, he was a postdoctoral fellow at the Department of Geosciences, Princeton University and a Writing in Science and Engineering fellow at the Princeton Writing Program. Umair received the KFUPM Early Career Researcher award and the Excellence in Teaching award in 2022 and 2023, respectively.