Scientific Machine Learning (SciML): An overview and discussion of applications in petroleum engineering

John F. Foster
Friday, February 5, 2021, 12 – 1pm

Scientific Machine Learning or SciML is a relatively new phrase that is used to describe the intersection of data science, machine learning, and physics-based computational simulation. SciML encompasses many ideas including physics informed neural networks, universal differential equations, and the use of synthetic data generated from physical simulators in training machine learning models for rapid decision making. This talk will give an overview of SciML using simple examples and discuss recent results from our investigations using SciML in petroleum engineering applications, specifically for reservoir simulation and drillstring dynamics.


John T. Foster is an associate professor and George H. Fancher Fellow in The Hildebrand Department of Petroleum and Geosystems Engineering. He also has appointments in The Department of Aerospace Engineering and Engineering Mechanics and as a core faculty member at the Oden Institute for Computational Engineering and Sciences at The University of Texas at Austin. Before joining UT-Austin, he was previously a faculty member in mechanical engineering at UTSA and was a Senior Member of the Technical Staff at Sandia National Laboratories where he worked for 7 years. He received his BS and MS in mechanical engineering from Texas Tech University and PhD from Purdue University. He is a registered Professional Engineer in the State of Texas and the co-founder and CTO of Daytum, a tech-enabled professional education company for data science and machine learning targeting the energy industry. During his career in research he has been involved in many projects ranging from full scale field tests, to laboratory experiments, to modeling and simulation efforts using some of the world’s largest computers. His research interests are in experimental and computational mechanics, scientific machine learning, and multi-scale modeling with applications to geomechanics, impact mechanics, fracture mechanics, and anomalous transport processes.

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