ANALYSIS OF THE PHONON DENSITY OF STATES IN IRON SULFIDE MATERIALS USING A DEEP LEARNING APPROACH WITH EUCLIDEAN NEURAL NETWORKS
The author uses deep learning with Euclidean Neural Networks to predict the phonon density of states (PhDOS) of hexagonal FeS material. The aim is to understand phonon dynamics and the influence of structural and compositional factors on the thermal and mechanical properties of the material. Test...
Saved in:
Main Author: | Dwi Fitriani, Nita |
---|---|
Format: | Theses |
Language: | Indonesia |
Online Access: | https://digilib.itb.ac.id/gdl/view/81532 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Institut Teknologi Bandung |
Language: | Indonesia |
Similar Items
-
Coherent phonon and charge dynamics in antimony sulfide.
by: Chong, Wee Kiang.
Published: (2013) -
Determination of the phonon density of states from specific heat data
by: Kok, W.C.
Published: (2014) -
Human face recognition by Euclidean distance and neural network
by: Chomtip Pornpanomchai, et al.
Published: (2018) -
MACHINE LEARNING IN NON-EUCLIDEAN SPACE
by: LI YIMIN
Published: (2021) -
Joint hyperbolic and Euclidean geometry contrastive graph neural networks
by: XU, Xiaoyu, et al.
Published: (2022)