This project brings together ASCR and HEP researchers to develop and apply new methods and algorithms in the area of extreme-scale inference and machine learning. The research program melds high-performance computing and techniques for “big data” analysis to enable new avenues of scientific discovery.
Deep transfer learning to automatically segment the precipitate from matrix in 3D Atom Probe Tomography data.
Employing architechtures inspired by insect brain to devise efficient, life-long learning machines.
Develop cross-cutting artificial intelligence framework for fast inference and training on heterogeneous computing resources as well as algorithmic advances in AI explainability and uncertainty quantification.
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.