Predicting the mechanical response of oligocrystals using deep convolutional neural networks.
Conference
·
OSTI ID:1641050
Abstract not provided.
- Research Organization:
- Sandia National Lab. (SNL-CA), Livermore, CA (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA)
- DOE Contract Number:
- AC04-94AL85000
- OSTI ID:
- 1641050
- Report Number(s):
- SAND2019-7582C; 677046
- Resource Relation:
- Conference: Proposed for presentation at the US National Congress on Computational Mechanics held July 28 - August 1, 2019 in Austin, Texas, United States of America.
- Country of Publication:
- United States
- Language:
- English
Similar Records
Convolutional Neural Networks for Frequency Response Predictions.
Drainage Network Generation using Deep Convolutional Generative Adversarial Neural Networks.
Denoising of Seismic Signals Recorded at Local to Near-Regional Distances Using Deep Convolutional Neural Networks.
Conference
·
Mon May 01 00:00:00 EDT 2017
·
OSTI ID:1641050
+4 more
Drainage Network Generation using Deep Convolutional Generative Adversarial Neural Networks.
Conference
·
Sat Dec 01 00:00:00 EST 2018
·
OSTI ID:1641050
+1 more
Denoising of Seismic Signals Recorded at Local to Near-Regional Distances Using Deep Convolutional Neural Networks.
Conference
·
Sun Mar 01 00:00:00 EST 2020
·
OSTI ID:1641050
+2 more