A deep material network approach for predicting the thermomechanical response of composites
Journal Article
·
· Composites. Part B, Engineering
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA), Office of Defense Nuclear Nonproliferation
- OSTI ID:
- 2267591
- Journal Information:
- Composites. Part B, Engineering, Journal Name: Composites. Part B, Engineering Vol. 272 Journal Issue: C; ISSN 1359-8368
- Publisher:
- ElsevierCopyright Statement
- Country of Publication:
- United Kingdom
- Language:
- English
Similar Records
Predicting the thermo-elasto-plastic response of composites using the Deep Material Network
Predicting infectious disease for biopreparedness and response: A systematic review of machine learning and deep learning approaches
Nuclear mass predictions for the crustal composition of neutron stars: A Bayesian neural network approach
Conference
·
2023
·
OSTI ID:2463055
+1 more
Predicting infectious disease for biopreparedness and response: A systematic review of machine learning and deep learning approaches
Journal Article
·
2022
· One Health
·
OSTI ID:2317676
+1 more
Nuclear mass predictions for the crustal composition of neutron stars: A Bayesian neural network approach
Journal Article
·
2016
· Physical Review C
·
OSTI ID:1235770