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]