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User Guidelines and Best Practices for CASL VUQ Analysis Using Dakota

Adams, Brian M.; Coleman, Kayla; Hooper, Russell; Khuwaileh, Bassam A.; Lewis, Allison; Smith, Ralph C.; Swiler, Laura P.; Turinsky, Paul J.; Williams, Brian W.

Sandia's Dakota software (available at http://dakota.sandia.gov) supports science and engineering transformation through advanced exploration of simulations. Specifically it manages and analyzes ensembles of simulations to provide broader and deeper perspective for analysts and decision makers. This enables them to enhance understanding of risk, improve products, and assess simulation credibility.

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TYPE SAND Report YEAR 2016
DOIOSTI

Fully-Coupled Thermo-Electrical Modeling and Simulation of Transition Metal Oxide Memristors

Mamaluy, Denis; Gao, Xujiao; Tierney, Brian D.

Transition metal oxide (TMO) memristors have recently attracted special attention from the semiconductor industry and academia. Memristors are one of the strongest candidates to replace flash memory, and possibly DRAM and SRAM in the near future. Moreover, memristors have a high potential to enable beyond-CMOS technology advances in novel architectures for high performance computing (HPC). The utility of memristors has been demonstrated in reprogrammable logic (cross-bar switches), brain-inspired computing and in non-CMOS complementary logic. Indeed, the potential use of memristors as logic devices is especially important considering the inevitable end of CMOS technology scaling that is anticipated by 2025. In order to aid the on-going Sandia memristor fabrication effort with a memristor design tool and establish a clear physical picture of resistance switching in TMO memristors, we have created and validated with experimental data a simulation tool we name the Memristor Charge Transport (MCT) Simulator.

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TYPE SAND Report YEAR 2016
DOIOSTI

POF-Darts: Geometric adaptive sampling for probability of failure

Reliability Engineering and System Safety

Ebeida, Mohamed; Mitchell, Scott A.; Swiler, Laura P.; Romero, Vicente J.; Rushdi, Ahmad A.

We introduce a novel technique, POF-Darts, to estimate the Probability Of Failure based on random disk-packing in the uncertain parameter space. POF-Darts uses hyperplane sampling to explore the unexplored part of the uncertain space. We use the function evaluation at a sample point to determine whether it belongs to failure or non-failure regions, and surround it with a protection sphere region to avoid clustering. We decompose the domain into Voronoi cells around the function evaluations as seeds and choose the radius of the protection sphere depending on the local Lipschitz continuity. As sampling proceeds, regions uncovered with spheres will shrink, improving the estimation accuracy. After exhausting the function evaluation budget, we build a surrogate model using the function evaluations associated with the sample points and estimate the probability of failure by exhaustive sampling of that surrogate. In comparison to other similar methods, our algorithm has the advantages of decoupling the sampling step from the surrogate construction one, the ability to reach target POF values with fewer samples, and the capability of estimating the number and locations of disconnected failure regions, not just the POF value. We present various examples to demonstrate the efficiency of our novel approach.

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TYPE Journal Article YEAR 2016
DOIOSTIScopus

On the consistency between nearest-neighbor peridynamic discretizations and discretized classical elasticity models

Computer Methods in Applied Mechanics and Engineering

Seleson, Pablo; Du, Qiang; Parks, Michael L.

The peridynamic theory of solid mechanics is a nonlocal reformulation of the classical continuum mechanics theory. At the continuum level, it has been demonstrated that classical (local) elasticity is a special case of peridynamics. Such a connection between these theories has not been extensively explored at the discrete level. This paper investigates the consistency between nearest-neighbor discretizations of linear elastic peridynamic models and finite difference discretizations of the Navier–Cauchy equation of classical elasticity. Although nearest-neighbor discretizations in peridynamics have been numerically observed to present grid-dependent crack paths or spurious microcracks, this paper focuses on a different, analytical aspect of such discretizations. We demonstrate that, even in the absence of cracks, such discretizations may be problematic unless a proper selection of weights is used. Specifically, we demonstrate that using the standard meshfree approach in peridynamics, nearest-neighbor discretizations do not reduce, in general, to discretizations of corresponding classical models. We study nodal-based quadratures for the discretization of peridynamic models, and we derive quadrature weights that result in consistency between nearest-neighbor discretizations of peridynamic models and discretized classical models. The quadrature weights that lead to such consistency are, however, model-/discretization-dependent. We motivate the choice of those quadrature weights through a quadratic approximation of displacement fields. The stability of nearest-neighbor peridynamic schemes is demonstrated through a Fourier mode analysis. Finally, an approach based on a normalization of peridynamic constitutive constants at the discrete level is explored. This approach results in the desired consistency for one-dimensional models, but does not work in higher dimensions. The results of the work presented in this paper suggest that even though nearest-neighbor discretizations should be avoided in peridynamic simulations involving cracks, such discretizations are viable, for example for verification or validation purposes, in problems characterized by smooth deformations. Moreover, we demonstrate that better quadrature rules in peridynamics can be obtained based on the functional form of solutions.

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TYPE Journal Article YEAR 2016
DOIOSTIScopus

Quantifying neural information content: A case study of the impact of hippocampal adult neurogenesis

Proceedings of the International Joint Conference on Neural Networks

Vineyard, Craig M.; Verzi, Stephen J.; James, Conrad D.; Aimone, James B.

Through various means of structural and synaptic plasticity enabling online learning, neural networks are constantly reconfiguring their computational functionality. Neural information content is embodied within the configurations, representations, and computations of neural networks. To explore neural information content, we have developed metrics and computational paradigms to quantify neural information content. We have observed that conventional compression methods may help overcome some of the limiting factors of standard information theoretic techniques employed in neuroscience, and allows us to approximate information in neural data. To do so we have used compressibility as a measure of complexity in order to estimate entropy to quantitatively assess information content of neural ensembles. Using Lempel-Ziv compression we are able to assess the rate of generation of new patterns across a neural ensemble's firing activity over time to approximate the information content encoded by a neural circuit. As a specific case study, we have been investigating the effect of neural mixed coding schemes due to hippocampal adult neurogenesis.

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TYPE Other Report YEAR 2016
DOIOSTIScopus
Results 4401–4450 of 9,998
Results 4401–4450 of 9,998