Using Deep Learning to Predict Fracture Patterns in Crystalline Solids
Fracture is a catastrophic and complex process that involves various time and length scales. Scientists have devoted vast efforts toward understanding the underlying mechanisms for centuries, with much work left in terms of predictability of models and fundamental understanding. To this end, we pres...
Main Authors: | Hsu, Yu-Chuan, Yu, Chi-Hua, Buehler, Markus J |
---|---|
Other Authors: | Massachusetts Institute of Technology. Laboratory for Atomistic and Molecular Mechanics |
Format: | Article |
Language: | English |
Published: |
Elsevier BV
2021
|
Online Access: | https://hdl.handle.net/1721.1/132724 |
Similar Items
-
Deep learning model to predict fracture mechanisms of graphene
by: Lew, Andrew James, et al.
Published: (2021) -
Tuning Mechanical Properties in Polycrystalline Solids Using a Deep Generative Framework
by: Hsu, Yu-Chuan, et al.
Published: (2022) -
Deep learning model to predict complex stress and strain fields in hierarchical composites
by: Yang, Zhenze, et al.
Published: (2021) -
End-to-end prediction of multimaterial stress fields and fracture patterns using cycle-consistent adversarial and transformer neural networks
by: Buehler, Eric L, et al.
Published: (2023) -
Linking atomic structural defects to mesoscale properties in crystalline solids using graph neural networks
by: Yang, Zhenze, et al.
Published: (2023)