Co-design approach that encompasses neuromorphic computing, systems architecture, and datacentric applications. Focus on high energy physics (HEP) and nuclear physics (NP) detector experiments
Deep transfer learning to automatically segment the precipitate from matrix in 3D Atom Probe Tomography data.
Multi-dimensional automated scalability tests, program analysis, performance learning and prediction at various levels of the software/hardware stack.
he project goal is to develop a probabilistic ML framework – PRISM – to improve manufacturing efficiency and demonstrate the technologies on wing spars.
The objective of RAPIDS2 is to assist the Office of Science (SC) application teams in overcoming computer science, data, and AI challenges in the use of DOE supercomputing resources to achieve scientific breakthroughs.
The goal of RAPIDS (a SciDAC Institute for Resource and Application Productivity through computation, Information, and Data Science) institute is to assist Office of Science (SC) application teams in overcoming computer science and data challenges in the use of DOE supercomputing resources to achieve science breakthroughs.