Sept |
Physics Guided Machine Learning: A New Paradigm for Modeling Science and Engineering Problems
Speaker: Vipin Kumar, Regents Professor and William Norris Endowed Chair, Department of Computer Science and Engineering, University of Minnesota
When: 2:00PM ~ 3:00PM, Wednesday, September 19, 2018
Where: Morgan Library Event Hall
Contact: Imme Ebert-Uphoff (iebert@colostate.edu)
Abstract: Physics-based models of dynamical systems are often used to study engineering and environmental systems. Despite their extensive use, these models have several well-known limitations due to incomplete or inaccurate representations of the physical processes being modeled. Given rapid data growth due to advances in sensor technologies, there is a tremendous opportunity to systematically advance modeling in these domains by using machine learning (ML) methods. However, capturing this opportunity is contingent on a paradigm shift in data-intensive scienti c discovery since the “black box” use of ML often leads to serious false discoveries in scienti c applications. Because the hypothesis space of scienti c applications is often complex and exponentially large, an uninformed data-driven search can easily select a highly complex model that is neither generalizable nor physically interpretable, resulting in the discovery of spurious relationships, predictors, and patterns. This problem becomes worse when there is a scarcity of labeled samples, which is quite common in science and engineering domains.
This talk makes a case that in real-world systems that are governed by physical processes, there is an opportunity to take advantage of fundamental physical principles to inform the search of a physically meaningful and accurate ML model. Even though this will be illustrated in the context of two problems in modeling water quality, the paradigm has the potential to greatly advance the pace of discovery in a number of scienti c and engineering disciplines where physics-based models are used, e.g., power engineering, climate science, weather forecasting, materials science, computational chemistry, and biomedicine.
Bio: Vipin Kumar is a Regents Professor and hold the William Norris Chair in the Department of Computer Science and Engineering at the University of Minnesota. His research interests include data mining, high-performance computing, and their applications in Climate/Ecosystems and health care. He is currently leading an NSF Expedition project on understanding climate change using data science approaches. He has authored over 300 research articles, and co-edited or coauthored 10 books including the widely used text book “Introduction to Parallel Computing”, and “Introduction to Data Mining”. Kumar has served as chair/ co-chair for many international conferences and workshops in the area of data mining and parallel computing, including 2015 IEEE International Conference on Big Data, IEEE International Conference on Data Mining (2002), and International Parallel and Distributed Processing Symposium (2001). Kumar is a Fellow of the ACM, IEEE, AAAS, and SIAM. Kumar’s research has been honored by the ACM SIGKDD 2012 Innovation Award, which is the highest award for technical excellence in the eld of Knowledge Discovery and Data Mining (KDD), and the 2016 IEEE Computer Society Sidney Fernbach Award, one of IEEE Computer Society’s highest awards in high performance computing.