Speakers
Lekshmi Sreekala
(Max-Planck-Institut für Eisenforschung GmbH)
Pawan Goyal
(Max Planck Institute for Dynamics of Complex Technical Systems)
Description
In this poster, we present our research goals of a recently BiGmax funded project towards learning dynamics of scanning transmission electron microscopy (STEM) by incorporating physical consistency with phase-field models. The primary idea of this project is to develop machine learning (ML)-based modeling of an interpretable coarse-grained dynamic model utilizing in situ STEM video sequences fulfilling a suitable dynamical phase-field equation. The modeling approach aims to discover governing equations by utilizing the video sequence data and prior physics knowledge that is directly compatible with analytic theories or subsequent ML-based analysis.
Primary authors
Christian Liebscher
( Max-Planck-Institut für Eisenforschung GmbH)
Christoph Freysoldt
( Max-Planck-Institut für Eisenforschung GmbH)
Jaber Mianroodi
( Max-Planck-Institut für Eisenforschung GmbH)
Lekshmi Sreekala
(Max-Planck-Institut für Eisenforschung GmbH)
Ning Wang
( Max-Planck-Institut für Eisenforschung GmbH)
Pawan Goyal
(Max Planck Institute for Dynamics of Complex Technical Systems)
Peter Benner
(Max Planck Institute for Dynamics of Complex Technical Systems)
Sandeep Reddy Bukka
(Max Planck Institute for Dynamics of Complex Technical Systems)
