Physics-Informed Machine Learning: The Next Evolution in Neural Network Development
Machine Learning (ML) is one of the fastest-growing subsets in artificial intelligence. One of the persistent challenges to ML model development is data quality and availability. Many researchers across a range of disciplines have begun leveraging Partial Differential Equations (PDEs) in the physical, biological, and other hard sciences to answer otherwise intractable problems resulting from the lack of data availability and issues with data quality. This approach is appropriate. While PDEs are not the only answer to all binary or multi-classification problems in ML modeling, they provide a series of physics-inspired ML modeling methods capable of addressing a set of challenges that were previously difficult, if not impossible, to infer. This presentation will focus on PDE utility.
The 60-minute webinar concludes with a moderated live question-and-answer period.
Capitol Technology University offers this webinar as a complimentary, informational service. This webinar offers a Certificate of Attendance.
About the Presenter
Dr. Karriem Perry
Dr. Perry served 22 years in the United States Army as a Ranger and a Special Forces soldier and is currently a senior data scientist in the public sector. Dr. Perry holds a B.S. in Psychology from Ottawa University (KS), an M.S. in Data Analytics from Southern New Hampshire University, and a Ph.D. in Artificial Intelligence from Capitol Technology University. His research focus is in the areas of statistical-relational machine learning, relational quantum mechanics in quantum machine learning, and probabilistic graphical modeling theory. Dr. Perry stresses the need to bring a wider understanding of the broad technological implications associated with artificial intelligence to the growing veteran population and underrepresented rural and urban communities.