Co-design approach that encompasses neuromorphic computing, systems architecture, and datacentric applications. Focus on high energy physics (HEP) and nuclear physics (NP) detector experiments
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.
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.
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.