Updated software improves slicing for large-format 3D printing
Filter News
Area of Research
- (-) Biology and Environment (17)
- (-) Isotopes (3)
- (-) National Security (6)
- (-) Supercomputing (36)
- Advanced Manufacturing (4)
- Biological Systems (1)
- Clean Energy (52)
- Climate and Environmental Systems (4)
- Computer Science (2)
- Fusion and Fission (4)
- Fusion Energy (7)
- Materials (69)
- Materials for Computing (4)
- Neutron Science (45)
- Nuclear Science and Technology (30)
- Nuclear Systems Modeling, Simulation and Validation (1)
- Quantum information Science (2)
- Transportation Systems (1)
News Topics
- (-) Biomedical (15)
- (-) Environment (14)
- (-) Exascale Computing (4)
- (-) Materials Science (10)
- (-) Microscopy (2)
- (-) Nanotechnology (6)
- (-) Neutron Science (11)
- (-) Nuclear Energy (6)
- 3-D Printing/Advanced Manufacturing (4)
- Advanced Reactors (1)
- Artificial Intelligence (13)
- Big Data (14)
- Bioenergy (6)
- Biology (4)
- Biotechnology (1)
- Chemical Sciences (2)
- Clean Water (1)
- Climate Change (4)
- Computer Science (57)
- Coronavirus (13)
- Cybersecurity (6)
- Decarbonization (1)
- Energy Storage (3)
- Frontier (3)
- Fusion (1)
- Grid (5)
- High-Performance Computing (2)
- Isotopes (4)
- Machine Learning (5)
- Materials (2)
- Mathematics (1)
- Mercury (1)
- Molten Salt (1)
- National Security (2)
- Physics (3)
- Polymers (1)
- Quantum Science (12)
- Security (5)
- Space Exploration (2)
- Summit (24)
- Sustainable Energy (8)
- Transportation (3)
Media Contacts
Using artificial neural networks designed to emulate the inner workings of the human brain, deep-learning algorithms deftly peruse and analyze large quantities of data. Applying this technique to science problems can help unearth historically elusive solutions.
OAK RIDGE, Tenn., March 4, 2019—A team of researchers from the Department of Energy’s Oak Ridge National Laboratory Health Data Sciences Institute have harnessed the power of artificial intelligence to better match cancer patients with clinical trials.
A team of scientists led by Oak Ridge National Laboratory used machine learning methods to generate a high-resolution map of vegetation growing in the remote reaches of the Alaskan tundra.