Co-design approach that encompasses neuromorphic computing, systems architecture, and datacentric applications. Focus on high energy physics (HEP) and nuclear physics (NP) detector experiments
The project overarching objective is to develop the simulation capability and to perform extended MHD and (drift-gyro) kinetic simulations of non-ELMing (and some ELMing) regime operating points to close gaps in understanding, prediction, and optimization of edge stability for an FPP. I am leading a team to develop ML techniques for extracting reduced-order models, data reduction, and feature extraction to the existing non-ELM database (based on interpolation of data), and to extrapolate to new parameter regimes (such as coil currents for negative-triangularity shaping) for ELM-free optimization.
The overarching objective of this SciDAC-5 project is to create consistent predictions of the dark and visible Universe across redshifts, length scales and wavebands based on state-of-the-art cosmological simulations. The simulation suite will encompass large-volume, high-resolution gravity-only simulations and hydrodynamical simulations equipped with a comprehensive set of subgrid models covering both small and large volumes. The simulations will be coupled to a powerful analysis framework and associated tools to maximize the analysis flexibility and science return.
Multi-dimensional automated scalability tests, program analysis, performance learning and prediction at various levels of the software/hardware stack.
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.