Modeling Atomistic Dynamic Fracture Mechanisms Using a Progressive Transformer Diffusion Model
出版年份 2022 全文链接
标题
Modeling Atomistic Dynamic Fracture Mechanisms Using a Progressive Transformer Diffusion Model
作者
关键词
-
出版物
JOURNAL OF APPLIED MECHANICS-TRANSACTIONS OF THE ASME
Volume 89, Issue 12, Pages -
出版商
ASME International
发表日期
2022-09-21
DOI
10.1115/1.4055730
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- FieldPerceiver: Domain agnostic transformer model to predict multiscale physical fields and nonlinear material properties through neural ologs
- (2022) Markus J. Buehler Materials Today
- Deep learning model to predict complex stress and strain fields in hierarchical composites
- (2021) Zhenze Yang et al. Science Advances
- LAMMPS - A flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales
- (2021) Aidan P. Thompson et al. COMPUTER PHYSICS COMMUNICATIONS
- Generative adversarial networks for transition state geometry prediction
- (2021) Małgorzata Z. Makoś et al. JOURNAL OF CHEMICAL PHYSICS
- Machine learning-based microstructure prediction during laser sintering of alumina
- (2021) Jianan Tang et al. Scientific Reports
- A deep learning augmented genetic algorithm approach to polycrystalline 2D material fracture discovery and design
- (2021) Andrew J. Lew et al. Applied Physics Reviews
- Words to Matter: De novo Architected Materials Design Using Transformer Neural Networks
- (2021) Zhenze Yang et al. Frontiers in Materials
- Re-epithelialization and immune cell behaviour in an ex vivo human skin model
- (2020) Ana Rakita et al. Scientific Reports
- Assembly of Foldable 3D Microstructures Using Graphene Hinges
- (2020) Seungyun Lim et al. ADVANCED MATERIALS
- Mechanical MNIST: A benchmark dataset for mechanical metamodels
- (2020) Emma Lejeune Extreme Mechanics Letters
- A concurrent multiscale study of dynamic fracture
- (2020) Qi Tong et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- Anisotropic Fracture Dynamics Due to Local Lattice Distortions
- (2019) Gang Seob Jung et al. ACS Nano
- The machine learning revolution in materials?
- (2019) Kristofer G. Reyes et al. MRS BULLETIN
- Recent advances and applications of machine learning in solid-state materials science
- (2019) Jonathan Schmidt et al. npj Computational Materials
- Can machine learning find extraordinary materials?
- (2019) Steven K. Kauwe et al. COMPUTATIONAL MATERIALS SCIENCE
- A Review of the Autoencoder and Its Variants: A Comparative Perspective from Target Recognition in Synthetic-Aperture Radar Images
- (2018) Ganggang Dong et al. IEEE Geoscience and Remote Sensing Magazine
- Fracture mechanics of monolayer molybdenum disulfide
- (2015) Xiaonan Wang et al. NANOTECHNOLOGY
- Interface structure and mechanics between graphene and metal substrates: a first-principles study
- (2010) Zhiping Xu et al. JOURNAL OF PHYSICS-CONDENSED MATTER
Create your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create NowBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started