David Mebane, West Virginia University
Connecting data and physical models with embedded machine learning at electrochemical interfaces and beyond
Host: Brian Popp
Associate Professor
Department of Mechanical, Materials and Aerospace Engineering
West Virginia University
As data-driven methods become more prominent throughout science, we need new ways
of combining physical understanding with data from experiments and first principles
calculations. This talk will present a unique paradigm for combining physical models
and data-driven elements, in which embedded data-driven functions represent well-defined
physical quantities, subject to independent measurement and calculation. A fast-evaluating,
decomposable Gaussian process is an enabling development. Examples to be discussed
include learning inhomogeneous free energy functions at ionically charged surfaces
and interfaces from electron and scanning probe microscope data.