Machine Learning for Accelerating Direct-Simulation Monte-Carlo Collision Operators.
Conference
·
OSTI ID:1811433
Abstract not provided.
- Research Organization:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA)
- DOE Contract Number:
- AC04-94AL85000
- OSTI ID:
- 1811433
- Report Number(s):
- SAND2020-7567C; 687574
- Resource Relation:
- Conference: Proposed for presentation at the Sandia Machine Learning and Deep Learning Workshop held August 3-7, 2020 in Albuquerque, NM, US.
- Country of Publication:
- United States
- Language:
- English
Similar Records
Machine Learning for Accelerating Direct-Simulation Monte-Carlo Collision Operators.
Finite Element Particle-In-Cell Direct Simulation Monte Carlo Simulations of Optically Triggered Nanosecond Electrical Switches.
Particle-In-Cell Direct-Simulation-Monte-Carlo Simulations of Optically Triggered Nanosecond Electrical Switches.
Conference
·
2021
·
OSTI ID:1884411
Finite Element Particle-In-Cell Direct Simulation Monte Carlo Simulations of Optically Triggered Nanosecond Electrical Switches.
Conference
·
2018
·
OSTI ID:1529477
Particle-In-Cell Direct-Simulation-Monte-Carlo Simulations of Optically Triggered Nanosecond Electrical Switches.
Conference
·
2018
·
OSTI ID:1592534