This is an emerging area whose predictive capability is partially based on modern data analysis and machine learning techniques rather than strictly on approximate solutions to equations that state fundamental physical principles or reduced semiempirical models. This activity encompasses a broad range of research areas and techniques, some of which are only recently coming into maturity in the context of high-end simulation.
Optimizing Stochastic Grid Dynamics at Exascale
Objective: Reliable and Efficient Planning of the Power Grid
Optimize power grid planning, operation, and control and improve reliability and efficiency
Lead: Pacific Northwest National Laboratory (PNNL)
Principal Investigators: Chris Oehmen, Pacific Northwest National Laboratory
Exascale Deep Learning–Enabled Precision Medicine for Cancer
Objective: Accelerate and Translate Cancer Research
Develop pre-clinical drug response models, predict mechanisms of RAS/RAF driven cancers, and develop treatment strategies
Lead: Argonne National Laboratory
Principal Investigators: Rick Stevens, Argonne National Laboratory
Exascale Solutions for Microbiome Analysis
Objective: Metagenomics for Analysis of Biogeochemical Cycles
Discover knowledge useful for environmental remediation and the manufacture of novel chemicals and medicines
Lead: Lawrence Berkeley National Laboratory
Principal Investigators: Katherine Yelick, Lawrence Berkeley National Laboratory
Data Analytics at Exascale for Free Electron Lasers
Objective: Light Source– Enabled Analysis of Protein and Molecular Structures and Design
Process data without beam time loss; determine nanoparticle size and shape changes; engineer functional properties in biology and material science
Lead: SLAC National Accelerator Laboratory
Principal Investigators: Amedeo Perazzo, SLAC National Accelerator Laboratory