We successfully utilized OCLF ORNL GPU computing resources for efficient uncertainty analysis, which addressed the computational overhead caused by our proposed probabilistic models.
Filter Research Highlights
Area of Research
- Advanced Manufacturing (1)
- Biological Systems (3)
- Building Technologies (1)
- Clean Energy (6)
- Climate and Environmental Systems (3)
- Computational Biology (1)
- Computational Chemistry (3)
- Computational Engineering (8)
- Computer Science (111)
- (-) Data (8)
- Energy Sciences (3)
- (-) Engineering Analysis (1)
- Geographic Information Science and Technology (1)
- (-) Materials (7)
- Materials for Computing (4)
- Mathematics (11)
- (-) Quantum information Science (10)
- Renewable Energy (2)
- Sensors and Controls (1)
- Supercomputing (35)
- (-) Visualization (3)
A team of Oak Ridge National Laboratory (ORNL) scientists involved in research topics of cybersecurity, statistical approaches, control systems, and dynamical models, reported a basic approach to security of physical systems that are interfaced with IT
A graph convolutional neural network (GCNN) was trained to accurately predict formation energy and mechanical properties of solid solution alloys crystallized in different lattice structures, thereby advancing the design of alloys for improving mechanic
Analyzing the logs of even the smallest Information Technology (IT) system can be a challenge, considering that they can generate millions of lines of log data in a very short time.
This work develops an approach for engineering non-Gaussian photonic states in discrete frequency bins.
Researchers from ORNL, Stanford University, and Purdue University developed and demonstrated a novel, fully functional quantum local area network (QLAN).