Machine learning-based probabilistic I/O performance models that take the background traffic, and system state into account while prediciting application performance on HPC system.
Deelop a framework for efficient and accurate equilibrium reconstructions, by automating and maximizing the information extracted from measurements and by leveraging physics-informed ML models constructed from experimental and synthetic solution databases to guide the search for the solution vector.
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.