Speaker
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.
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Primary authors
Pawan Goyal
(Max Planck Institute for Dynamics of Complex Technical Systems)
Christoph Freysoldt
(MPI Eisenforschung)
Dr
Christian Liebscher
(Max-Planck-Institut für Eisenforschung)
Jaber Mianroodi
Peter Benner
