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UID:submissions.supercomputing.org_SC23_sess253_exforum115@linklings.com
SUMMARY:Digital Twins for Science
DESCRIPTION:Tom Gibbs (NVIDIA Corporation)\n\nDigital Twins have emerged a
 s one of the hot new modeling concepts in HPC as we enter the Post Exascal
 e Era. Originally conceived as a modeling tool for manufacturing and Produ
 ct Life Cycle Management Digital Twins are evolving in HPC with the conver
 gence of simulation, machine learning and live data. The introduction of m
 achine learning into the HPC workflows has been a critical component in th
 e evolution of the Digital Twin for Science where for the first time model
 s with fidelity at the atomic level can scale to the full scope of a physi
 cal object or system.\n\nThe talk will include examples from physics, biol
 ogy and climate science and describe how the NVIDIA platform can address t
 he requirements for first principles simulation using the HPC SDK, the RAP
 IDS SDK and Modulus to develop the robust machine learning models, Holosca
 n our tool set to enable data acquisition from live data sources and Omniv
 erse our SDK to aggregate the workflow components and visualize it.\n\nTag
 : Artificial Intelligence/Machine Learning, Fault Handling and Tolerance, 
 Large Scale Systems, Programming Frameworks and System Software\n\nRegistr
 ation Category: Tech Program Reg Pass, Exhibits Reg Pass\n\nSession Chair:
  Nathan Hanford (Lawrence Livermore National Laboratory (LLNL))\n\n
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