Speaker
Christoph Freysoldt
(MPI Eisenforschung)
Description
To extract transferable insights from scanning transmission electron microscopy (STEM), one must deal with noise arising from electron scattering and of the investigated sample. This noise hinders a quantitative analysis of the observation, notably when the features of interest lie in the gradients of the raw data. Physics-informed neural networks have been proposed as a means to incorporate compliance with physical equations that are chosen a priori. We show here that phase field models can help to efficiently coarse-grain STEM video sequences of phase transformations.
Primary authors
Dr
Ning Wang
(MPI Eisenforschung)
Christoph Freysoldt
(MPI Eisenforschung)
Co-authors
Dr
Wenjun Lu
(MPI Eisenforschung)
Dr
Christian Liebscher
(Max-Planck-Institut für Eisenforschung)
