5 citations · 5 across the 3 of their papers we have counts for
3 papers
Instance Segmentation of Dislocations in TEM Images
Karina Ruzaeva, Kishan Govind, Marc Legros +1
Quantitative Transmission Electron Microscopy (TEM) during in-situ straining experiment is able to reveal the motion of dislocations -- linear defects in the crystal lattice of met…
Efficient Surrogate Models for Materials Science Simulations: Machine Learning-based Prediction of Microstructure Properties
Binh Duong Nguyen, Pavlo Potapenko, Aytekin Dermici +3
Determining, understanding, and predicting the so-called structure-property relation is an important task in many scientific disciplines, such as chemistry, biology, meteorology, p…
Deep Learning of Crystalline Defects from TEM images: A Solution for the Problem of "Never Enough Training Data"
Kishan Govind, Daniela Oliveros, Antonin Dlouhy +2
Crystalline defects, such as line-like dislocations, play an important role for the performance and reliability of many metallic devices. Their interaction and evolution still pose…