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TAFI/Kebab End of Project Report

Laros, James H.; Wisniewski, Kyra L.; Ward, Katrina J.; Khanna, Kanad K.

This report focuses on the two primary goals set forth in Sandia’s TAFI effort, referred to here under the name Kebab. The first goal is to overlay a trajectory onto a large database of historical trajectories, all with very different sampling rates than the original track. We demonstrate a fast method to accomplish this, even for databases that hold over a million tracks. The second goal is to then demonstrate that these matched historical trajectories can be used to make predictions about unknown qualities associated with the original trajectory. As part of this work, we also examine the problem of defining the qualities of a trajectory in a reproducible way.

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TYPE Other Report YEAR 2021
DOIOSTI

The DPG Method for the Convection-Reaction Problem Revisited

Demkowicz, Leszek; Roberts, Nathan V.

We study both conforming and non-conforming versions of the practical DPG method for the convection-reaction problem. We determine that the most common approach for DPG stability analysis (construction of a local Fortin operator) is infeasible for the convection-reaction problem. We then develop a line of argument based on the direct construction of a global Fortin operator; we find that employing a polynomial enrichment for the test space does not suffice for this purpose, motivating the introduction of a (two-element) subgrid mesh. The argument combines mathematical analysis with numerical experiments

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TYPE Other Report YEAR 2021
DOIOSTI

Parallel Solver Framework for Mixed-Integer PDE-Constrained Optimization

Phillips, Cynthia A.; Chatter, Michelle A.; Eckstein, Jonathan; Erturk, Alper; El-Kady, I.; Gerbe, Romain; Kouri, Drew P.; Loughlin, William; Reinke, Charles M.; Rokkam, Rohith; Ruzzene, Massimo; Sugino, Christopher; Swanson, Calvin; van Bloemen Waanders, Bart G.

ROL-PEBBL is a C++, MPI-based parallel code for mixed-integer PDE-constrained optimization (MIPDECO). In these problems we wish to optimize (control, design, etc.) physical systems, which must obey the laws of physics, when some of the decision variables must take integer values. ROL-PEBBL combines a code to efficiently search over integer choices (PEBBL = Parallel Enumeration Branch-and-Bound Library) and a code for efficient nonlinear optimization, including PDE-constrained optimization (ROL = Rapid Optimization Library). In this report, we summarize the design of ROL-PEBBL and initial applications/results. For an artificial source-inversion problem, finding sources of pollution on a grid from sparse samples, ROL-PEBBLs solution for the nest grid gave the best optimization guarantee for any general solver that gives both a solution and a quality guarantee.

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TYPE SAND Report YEAR 2021
DOIOSTI

Inelastic peridynamic model for molecular crystal particles

Computational Particle Mechanics

Silling, Stewart A.; Barr, Christopher M.; Cooper, Marcia A.; Lechman, Jeremy B.; Bufford, Daniel C.

The peridynamic theory of solid mechanics is applied to modeling the deformation and fracture of micrometer-sized particles made of organic crystalline material. A new peridynamic material model is proposed to reproduce the elastic–plastic response, creep, and fracture that are observed in experiments. The model is implemented in a three-dimensional, meshless Lagrangian simulation code. In the small deformation, elastic regime, the model agrees well with classical Hertzian contact analysis for a sphere compressed between rigid plates. Under higher load, material and geometrical nonlinearity is predicted, leading to fracture. Finally, the material parameters for the energetic material CL-20 are evaluated from nanoindentation test data on the cyclic compression and failure of micrometer-sized grains.

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TYPE Journal Article YEAR 2021
DOIOSTI

Using MLIR Framework for Codesign of ML Architectures Algorithms and Simulation Tools

Lewis, Cannada L.; Hughes, Clayton H.; Hammond, Simon D.; Rajamanickam, Sivasankaran R.

MLIR (Multi-Level Intermediate Representation), is an extensible compiler framework that supports high-level data structures and operation constructs. These higher-level code representations are particularly applicable to the artificial intelligence and machine learning (AI/ML) domain, allowing developers to more easily support upcoming heterogeneous AI/ML accelerators and develop flexible domain specific compilers/frameworks with higher-level intermediate representations (IRs) and advanced compiler optimizations. The result of using MLIR within the LLVM compiler framework is expected to yield significant improvement in the quality of generated machine code, which in turn will result in improved performance and hardware efficiency

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TYPE Other Report YEAR 2021
DOIOSTI

Robustness and Validation of Model and Digital Twins Deployment

Volkova, Svitana; Stracuzzi, David J.; Shafer, Jenifer; Ray, Jaideep R.; Pullum, Laura

For digital twins (DTs) to become a central fixture in mission critical systems, a better understanding is required of potential modes of failure, quantification of uncertainty, and the ability to explain a model’s behavior. These aspects are particularly important as the performance of a digital twin will evolve during model development and deployment for real-world operations.

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TYPE Other Report YEAR 2021
DOIOSTI

Data-driven learning of nonlocal physics from high-fidelity synthetic data

Computer Methods in Applied Mechanics and Engineering

You, Huaiqian; Yu, Yue; Trask, Nathaniel A.; Gulian, Mamikon G.; D'Elia, Marta D.

A key challenge to nonlocal models is the analytical complexity of deriving them from first principles, and frequently their use is justified a posteriori. In this work we extract nonlocal models from data, circumventing these challenges and providing data-driven justification for the resulting model form. Extracting data-driven surrogates is a major challenge for machine learning (ML) approaches, due to nonlinearities and lack of convexity — it is particularly challenging to extract surrogates which are provably well-posed and numerically stable. Our scheme not only yields a convex optimization problem, but also allows extraction of nonlocal models whose kernels may be partially negative while maintaining well-posedness even in small-data regimes. To achieve this, based on established nonlocal theory, we embed in our algorithm sufficient conditions on the non-positive part of the kernel that guarantee well-posedness of the learnt operator. These conditions are imposed as inequality constraints to meet the requisite conditions of the nonlocal theory. We demonstrate this workflow for a range of applications, including reproduction of manufactured nonlocal kernels; numerical homogenization of Darcy flow associated with a heterogeneous periodic microstructure; nonlocal approximation to high-order local transport phenomena; and approximation of globally supported fractional diffusion operators by truncated kernels.

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TYPE Journal Article YEAR 2021
DOIOSTIScopus
Results 851–900 of 9,998
Results 851–900 of 9,998