Emulating microstructural evolution during spinodal decomposition using a tensor decomposed convolutional and recurrent neural network
Phase-field (PF) models are one of the most powerful tools to simulate microstructural
evolution in metallic materials, polymers, and ceramics. However, existing PF approaches …
evolution in metallic materials, polymers, and ceramics. However, existing PF approaches …
A novel data-driven emulator for predicting electromigration-mediated damage in polycrystalline interconnects
Electromigration (EM)-induced diffusional transport of metal atoms, which can manifest as
the defects of grain boundary slits and voids in a metal line, often fail an entire electronic …
the defects of grain boundary slits and voids in a metal line, often fail an entire electronic …
Unsupervised machine learning algorithms as support tools in molecular dynamics simulations
Unsupervised Machine Learning algorithms such as clustering offer convenient features for
data analysis tasks. When combined with other tools like visualization software, the …
data analysis tasks. When combined with other tools like visualization software, the …
Novel Data-driven Emulator for Predicting Microstructure Evolutions
P Wu - 2024 - keep.lib.asu.edu
Phase-field (PF) models are one of the most powerful tools to simulate microstructural
evolution in metallic materials, polymers, and ceramics. However, existing PF approaches …
evolution in metallic materials, polymers, and ceramics. However, existing PF approaches …
[HTML][HTML] Cluster analysis for granular mechanics simulations using Machine Learning Algorithms
Molecular Dynamics (MD) simulations on grain collisions allow to incorporate complex
properties of dust interactions. We performed simulations of collisions of porous grains, each …
properties of dust interactions. We performed simulations of collisions of porous grains, each …