Synergies between Machine Learning and Physical Models

December 5, 2016 - December 9, 2016

Institute for Pure and Applied Mathematics (IPAM), UCLA, Los Angeles, CA

The application of machine learning (ML) to the computer simulation of materials has features that are somewhat uncommon in ML: the data is often free of noise, in principle unlimited amounts of data are available at known unit cost, and there is often considerable freedom in choosing data locations. This calls for the close examination of which ML strategies are best, and what their ultimate limitations are in practice. Can we create ML models of arbitrary accuracy? How can recent advances in on-line or active learning be utilized? What can more classical statistical interpolation methods contribute? This workshop will broadly address the reaches and limitations of ML as applied to the modeling of physical systems and highlight examples where physical models can be successfully combined or even derived from ML algorithms. The application deadline is Monday, October 10, 2016.