Working without data: overcoming gaps in deep learning and physics-based extrapolation

Despite its many recent successes, several fundamental issues remain with the application of deep learning to experimental data and first-principles based simulation.

These problems include the ability to extrapolate to unseen experimental conditions, transfer knowledge across length-scales, and the challenge of interpreting results within a physically motivated framework. Other challenges include the lack of a standardized methodology for reporting and understanding model errors as well as the frequent requirement for large quantities of data.

I will outline some of our ongoing efforts to address some of these challenges, with special attention paid to the concept of extrapolation (including the physical conditions of study and across length scales). To explore these ideas, we explore model spin-systems, 2d materials, and optical lattices.