Science-Application-Informed Machine Learning
Thanks to IT industry push, Machine Learning (ML) capabilities are in a phase of tremendous growth, and there is great opportunity to point these practically powerful tools toward modeling specific to applications, e.g. in natural and engineering sciences. The challenge is to incorporate domain expertise from traditional scientific discovery into next-generation ML models. We propose to develop new theoretical and algorithmic methodology that extends cutting-edge ML tools and merge them with application-specific knowledge stated in the form of constraints, symmetries, conservation laws, phenomenological assumptions and other examples of domain expertise regarding relevant degrees of freedom. The emerging methodology is illustrated on the following four enabling examples:
1. Topology and Parameter Estimation in Power Grids [IEEE CONES 2018/ arXiv:1710.10727]
2. Acceleration of Computational Fluid Dynamics with Deep Learning [APS/DFD2017 abstract + work in progress]
3. Learning Graphical Models [Science 2018/ arXiv:1612.05024] and NIPS2016/ arXiv:1605.07252]
4. Renormalization of Tensor Networks (Graphical Models) [AISTATS 2018/ arXiv:1801.01649 and ICML 2018/ arXiv:1803.05104]
Bio: Dr. Chertkov's areas of interest include statistical and mathematical physics and mathematics applied to energy and communication networks, machine learning, control theory, information theory, computer science, fluid mechanics and optics. Dr. Chertkov received his Ph.D. in physics from the Weizmann Institute of Science in 1996, and his M.Sc. in physics from Novosibirsk State University in 1990. After his Ph.D., Dr. Chertkov spent three years at Princeton University as a R.H. Dicke Fellow in the Department of Physics. He joined Los Alamos National Lab in 1999, initially as a J.R. Oppenheimer Fellow in the Theoretical Division. He is now a technical staff member in the same division. Dr. Chertkov has published more than 190 papers in these research areas. He is an editor of the Journal of Statistical Mechanics (JSTAT), associate editor of IEEE Transactions on Control of Network Systems, member of the Editorial Board of Scientific Reports (Nature Group), a fellow of the American Physical Society (APS) and a senior member of IEEE.