The Advanced Simulation and Computing (ASC) initiative on Advanced Machine Learning (AML) aims to maximize near and long-term impact of Artificial Intelligence (AI) and Machine Learning (ML) technologies on Sandia’s Nuclear Deterrence (ND) program. In this ASC-AML funded project, the team is developing new approaches for assessing the quality of machine learning predictions based on expensive/limited experimental data, with focus on problems in the nuclear deterrence (ND) mission domain. Guided by the classical Predictive Capability Maturity Model (PCMM) workflow for Verification, Validation, and Uncertainty Quantification (V&V/UQ), the project aims to rigorously assess the statistical properties of the input features when training a Scientific Machine Learning (SciML) model and examine the associated sources of noise, e.g., measurement noise. This will, in turn, enable the decomposition of output uncertainty into unavoidable aleatory part versus reducible model-form uncertainty. To improve Sandia’s stockpile surveillance analysis capabilities, the team uses signature waveforms collected using non-destructive functioning and identify the most-discriminative input features, in order to assess the quality of a training dataset. By further decomposing uncertainty into its aleatoric and epistemic components, the team will guide computational/sampling resources towards reducing the treatable parts of uncertainty. This workflow will enhance the overall credibility of the resulting predictions and open new doors for SciML models to be credibly deployed to costly or high stakes ND problems.

Workflow illustration of a machine learning classifier, trained on labeled time- or frequency-domain signals in order to classify new measurements, with uncertainty metrics.

Contact: Rushdi, Ahmad
June 2021
2021-7207 S

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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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Managing the modeling-simulation-analysis workflows that provide the basis for Sandia’s Nuclear Deterrence programs is a requirement for assuring verifiable, documented, and reproducible results. The Sandia Analysis Workbench (SAW) has now been extended to provide workflow management through the final tasks of ensemble analysis using Sandia’s Slycat framework. This new capability enhances multi-platform modeling-simulation workflows through the Next Generation Workflow (NGW) system. With the goal of providing end-to-end workflow capability to the nuclear deterrence programs, the new technology integrates Slycat and SAW. It fills the workflow gap between computational simulation results and post-processing tasks of ensemble analysis and the characterization of uncertainty quantification (UQ). This work compliments simulation data management while providing encapsulated, targeted sub-workflows for ensemble analysis, verification and validation, and UQ. The integration of Slycat management into SAW affords a common point of control and configuration. This connects analysis with modeling and simulation, and provides a documented provenance of that analysis. The heart of the work is a set of innovative NGW components that harvest ensemble features, quantities of interest (QoIs), simulation responses, and in situ generated images, videos, and surface meshes. These components are triggered on-demand by the workflow engine when the prerequisite data and conditions are satisfied. Executing from an HPC platform, the components apply those artifacts to generate parameterized, user-ready analyses on the Slycat server. These components can eliminate the need for analyst intervention to hand-process artifacts or QoIs. The technology automates data flow and evidence production needed for decision-support in quantification of margins and uncertainty. Finally, these components deliver an automated and repeatable shortcut to Slycat’s meta-analysis crucial for optimizing ensembles, evaluating parameter studies, and understanding sensitivity analysis.

An unsupervised ensemble analysis sub-workflow sits atop a larger NGW computational simulation workflow. In the background, a solid mechanics parameter study is being analyzed in Slycat.

Contact: Hunt, Warren L.
June 2021
2021-7273 S

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DOE/ASCR is investing over 5 years in Sandia to build and host the Quantum Scientific Computing Open User Testbed (QSCOUT): a quantum testbed based on trapped ions that is available to the research community (led by Susan Clark, 5225). As an open platform, it will not only provide full specifications and control for the realization of all high level quantum and classical processes, it will also enable researchers to investigate, alter, and optimize the internals of the testbed and test more advanced implementations of quantum operations. To maximize the usability and impact of QSCOUT, Sandia researchers in 1400 (Andrew Landahl, 1425) have led the development of the Jaqal quantum assembly language, which has been publicly released in conjunction with a QSCOUT emulator.  QSCOUT is currently hosting external user teams from UNM, ORNL, IBM, the University of Indiana, and the University of California at Berkeley for scientific discovery in quantum computing.

For more information contact qscout@sandia.gov or visit https://www.sandia.gov/quantum/Projects/QSCOUT.html :POC: Andrew Landahl

May 2021

Contact: Landahl, Andrew J
May 2021
2021-5654 S

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