Center for Computing Research (CCR)

Center for Computing Research

Computational Mathematics, 01442

The Computational Mathematics Department (1442) performs cutting edge research, driven by DOE needs, to develop the mathematical foundations and the algorithmic and software advances to enable accurate, predictive, and scalable computational simulation methods. We deliver comprehensive theoretical and computational tools that impact Sandia’s mission and push our capabilities beyond forward simulations. Members of the department interact and collaborate with a broad range of Sandia and DOE staff and also maintain a highly visible external research presence by collaborating with universities and industry, publishing peer-reviewed literature, participating in professional societies, and refereeing and editing for journals.

 

People

Michael L. Parks
Manager, Computational Mathematics
Email: mlparks@sandia.gov
Phone: 505/845-0512
Fax: 505/845-7442

Mailing address:
Sandia National Laboratories
P.O. Box 5800, MS 1320
Albuquerque, NM
87185-1320
DeVonna Skye Flanery, Office Administrative Assistant
Staff
Jonas Albert Actor
Luc Berger-Vergiat
Stephen D Bond
Eric Christopher Cyr
Amy Grace de Castro
Michael James Gaiewski
Graham Bennett Harper
Shuai NMN Jiang
Paul Allen Kuberry
Scott A. Mitchell
Peter Brian Ohm
Ravi Ghanshyam Patel
Mauro Perego
Kara J. Peterson
Nathan V. Roberts
John N. Shadid
Kenneth Chadwick Sockwell (Chad)
Ignacio Tomas
Nathaniel Albert Trask
Raymond S. Tuminaro

Projects

The Computational Mathematics Department is involved with the projects listed below.

Software

The Computational Mathematics Department is involved with the software listed below.

News

  • Investigating Arctic Climate Variability with Global Sensitivity Analysis of Low-resolution E3SM.

     As a first step in quantifying uncertainties in simulated Arctic climate response, Sandia researchers have performed a global sensitivity analysis (GSA) using a fully coupled ultralow-resolution configuration of the Energy Exascale Earth System Model (E3SM)....

    Investigating Arctic Climate Variability with Global Sensitivity Analysis of Low-resolution E3SM.

     As a first step in quantifying uncertainties in simulated Arctic climate response, Sandia researchers have performed a global sensitivity analysis (GSA) using a fully coupled ultralow-resolution configuration of the Energy Exascale Earth System Model (E3SM).  Coupled Earth system models are computationally expensive to run, making it difficult to generate the large ensembles required for uncertainty quantification.  In this research an ultralow version of E3SM was utilized to tractably investigate parametric uncertainty in the fully coupled model. More than one hundred perturbed simulation ensembles of one hundred years each were generated for the analysis and impacts on twelve Arctic quantities of interest were measured using the PyApprox library. The parameter variations show significant impact on the Arctic climate state with the largest impact coming from atmospheric parameters related to cloud parameterizations. To our knowledge, this is the first global sensitivity analysis involving the fully-coupled E3SM. The results will be used to inform model tuning work as well as targeted studies at higher resolution.

    Ultra-low atmosphere grid (left) and ultra-low ocean grid (right).

    Points of contact: Kara Peterson (kjpeter@sandia.gov)                Irina Tezaur (ikalash@sandia.gov)  

    For more information on E3SM: https://e3sm.org/  

     

     

    Ultra-low atmosphere grid (left) and ultra-low ocean grid (right).

    Contact: Peterson, Kara J.
    January 2021
    1267024

  • Machine Learning for Xyce Circuit Simulation

    The Advanced Simulation and Computing (ASC) initiative to maximize near and long-term Artificial Intelligence (AI) and Machine Learning (ML) technologies on Sandia’s Nuclear Deterrence (ND) program funded a project focused on producing physics-aware machine learned compact device models suited for use in production circuit simulators such as Xyce....

    Machine Learning for Xyce Circuit Simulation

    The Advanced Simulation and Computing (ASC) initiative to maximize near and long-term Artificial Intelligence (AI) and Machine Learning (ML) technologies on Sandia’s Nuclear Deterrence (ND) program funded a project focused on producing physics-aware machine learned compact device models suited for use in production circuit simulators such as Xyce. While the original goal was only to make a demonstration of these capabilities, the team worked closely with Xyce developers to ensure the resulting product would be suitable for the already large group of Xyce users both internal and external to Sandia. This was done by extending the existing C++ general external interface in Xyce and adding Pybind11 hooks. The result is that with release 7.3 of Xyce, the ability to define machine learned compact device models entirely in Python (the most commonly used machine learning language) and use them with Xyce will be publicly available.

    Proposed workflow for developing data-driven compact device models using Xyce and TensorFlow

    Contact: Kuberry, Paul Allen
    June 2021
    2021-7268 S

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