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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Multi-fidelity Gaussian process regression for prediction of random fields

We propose a new multi-fidelity Gaussian process regression (GPR) approach for prediction of random fields based on observations of surrogate models or hierarchies of surrogate models. Our method builds upon recent work on recursive Bayesian techniques, in particular recursive co-kriging, and extends it to vector-valued fields and various types of covariances, including separable and non-separable ones. The framework we propose is general and can be used to perform uncertainty propagation and quantification in model-based simulations, multi-fidelity data fusion, and surrogate-based optimization. We demonstrate the effectiveness of the proposed recursive GPR techniques through various examples. Specifically, we study the stochastic Burgers equation and the stochastic Oberbeck–Boussinesq equations describing natural convection within a square enclosure. In both cases we find that the standard deviation of the Gaussian predictors as well as the absolute errors relative to benchmark stochastic solutions are very small, suggesting that the proposed multi-fidelity GPR approaches can yield highly accurate results.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Parallel Markov Random Fields for Image Segmentation (PMRF-IS) v1.0

This software performs multi-label segmentation of 2D/3D images. It is highly accurate, results rely on use of an Markov random field (MRF) formulation. It runs in both shared and distributed memory parallel modes. MRF algorithms are powerful tools in image analysis to explore contextual information of data.

Heinemann, Colleen↗
DOE OSTI2024

Random fields from quenched disorder in an archetype for correlated electrons: The parallel spin stripe phase of La 1.6 – x Nd 0.4 Sr x CuO 4 at the 1/8 anomaly

The parallel stripe phase is remarkable both in its own right, and in relation to the other phases with which it coexists. Its inhomogeneous nature makes such states susceptible to random fields from quenched magnetic vacancies. Here we argue this is the case by introducing low concentrations of nonmagnetic Zn impurities (0%–10%) into La 1.6–x ⁢Nd 0.4⁢ Sr x ⁢CuO 4 (Nd-LSCO) with x=0.125 in single-crystal form, well below the percolation threshold of ~41% for a two-dimensional square lattice. Elastic neutron scattering measurements on these crystals show clear magnetic quasi-Bragg peaks at all Zn dopings. While all the Zn-doped crystals display order parameters that merge into each other and the background at ~68 K, the temperature dependence of the order parameter as a function of Zn concentration is drastically different. This result is consistent with meandering charge stripes within the parallel stripe phase, which are pinned in the presence of quenched magnetic vacancies. In turn it implies vacancies that preferentially occupy sites within the charge stripes, and hence that can be very effective at disrupting superconductivity in Nd-LSCO (x=0.125), and, by extension, in all systems exhibiting parallel stripes.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2017

Enhancing gene regulatory network inference through data integration with markov random fields

Here, a gene regulatory network links transcription factors to their target genes and represents a map of transcriptional regulation. Much progress has been made in deciphering gene regulatory networks computationally. However, gene regulatory network inference for most eukaryotic organisms remain challenging. To improve the accuracy of gene regulatory network inference and facilitate candidate selection for experimentation, we developed an algorithm called GRACE (Gene Regulatory network inference ACcuracy Enhancement). GRACE exploits biological a priori and heterogeneous data integration to generate high- confidence network predictions for eukaryotic organisms using Markov Random Fields in a semi-supervised fashion. GRACE uses a novel optimization scheme to integrate regulatory evidence and biological relevance. It is particularly suited for model learning with sparse regulatory gold standard data. We show GRACE’s potential to produce high confidence regulatory networks compared to state of the art approaches using Drosophila melanogaster and Arabidopsis thaliana data. In an A. thaliana developmental gene regulatory network, GRACE recovers cell cycle related regulatory mechanisms and further hypothesizes several novel regulatory links, including a putative control mechanism of vascular structure formation due to modifications in cell proliferation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Polynomial Chaos Surrogate Construction for Random Fields with Parametric Uncertainty

Engineering and applied science rely on computational experiments to rigorously study physical systems. The mathematical models used to probe these systems are highly complex, and sampling-intensive studies often require prohibitively many simulations for acceptable accuracy. Surrogate models provide a means of circumventing the high computational expense of sampling such complex models. In particular, polynomial chaos expansions (PCEs) have been successfully used for uncertainty quantification studies of deterministic models where the dominant source of uncertainty is parametric. We discuss an extension to conventional PCE surrogate modeling to enable surrogate construction for stochastic computational models that have intrinsic noise in addition to parametric uncertainty. We develop a PCE surrogate on a joint space of intrinsic and parametric uncertainty, enabled by Rosenblatt transformations, which are evaluated via kernel density estimation of the associated conditional cumulative distributions. Furthermore, we extend the construction to random field data via the Karhunen–Loève expansion. We then take advantage of closed-form solutions for computing PCE Sobol indices to perform a global sensitivity analysis of the model which quantifies the intrinsic noise contribution to the overall model output variance. Additionally, the resulting joint PCE is generative in the sense that it allows generating random realizations at any input parameter setting that are statistically approximately equivalent to realizations from the underlying stochastic model. The method is demonstrated on a chemical catalysis example model and a synthetic example controlled by a parameter that enables a switch from unimodal to bimodal response distributions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Discrete-Direct Model Calibration and Uncertainty Propagation Method Confirmed on Multi-Parameter Plasticity Model Calibrated to Sparse Random Field Data

A discrete direct (DD) model calibration and uncertainty propagation approach is explained and demonstrated on a 4-parameter Johnson-Cook (J-C) strain-rate dependent material strength model for an aluminum alloy. The methodology's performance is characterized in many trials involving four random realizations of strain-rate dependent material-test data curves per trial, drawn from a large synthetic population. The J-C model is calibrated to particular combinations of the data curves to obtain calibration parameter sets which are then propagated to “Can Crush” structural model predictions to produce samples of predicted response variability. These are processed with appropriate sparse-sample uncertainty quantification (UQ) methods to estimate various statistics of response with an appropriate level of conservatism. This is tested on 16 output quantities (von Mises stresses and equivalent plastic strains) and it is shown that important statistics of the true variabilities of the 16 quantities are bounded with a high success rate that is reasonably predictable and controllable. The DD approach has several advantages over other calibration-UQ approaches like Bayesian inference for capturing and utilizing the information obtained from typically small numbers of replicate experiments in model calibration situations—especially when sparse replicate functional data are involved like force–displacement curves from material tests. The DD methodology is straightforward and efficient for calibration and propagation problems involving aleatory and epistemic uncertainties in calibration experiments, models, and procedures.

42 ENGINEERING↗
DOE OSTI2021

Beyond magnons in Nd 2 ScNbO 7 : An Ising pyrochlore antiferromagnet with all-in–all-out order and random fields

We report the low-temperature magnetic properties of Nd 3+ pyrochlore Nd 2 ScNbO 7 . Susceptibility and magnetization show an easy-axis moment, and heat capacity reveals a phase transition to long-range order at T N =371(2) mK with a fully recovered ΔS=Rln(2), 53% of it recovered for T>T N . Elastic neutron scattering shows a long-range all-in all-out magnetic order with low-Q diffuse elastic scattering. Inelastic neutron scattering shows a low-energy flat band, indicating a magnetic Hamiltonian similar to Nd 2 Zr 2 O 7 . Nuclear hyperfine excitations measured by ultra-high-resolution neutron backscattering indicate a distribution of static electronic moments below T N , which may be due to B-site disorder influencing Nd crystal electric fields. Analysis of heat-capacity data shows an unexpected T-linear or T 3/2 term which is inconsistent with conventional magnon quasiparticles, but is consistent with fractionalized spinons or gapless local spin excitations. We use legacy data to show similar behavior in Nd 2 Zr 2 O 7 . Comparing local static moments also reveals a suppression of the nuclear Schottky anomaly in temperature, evidencing a fraction of Nd sites with nearly zero static moment, consistent with exchange-disorder-induced random singlet formation. Finally, taken together, these measurements suggest an unusual fluctuating magnetic ground state which mimics a spin liquid, but may not actually be one.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2017

Robust Spectral Unmixing of Sparse Multispectral Lidar Waveforms using Gamma Markov Random Fields

Here, this paper presents a new Bayesian spectral un-mixing algorithm to analyse remote scenes sensed via sparse multispectral Lidar measurements. To a first approximation, in the presence of a target, each Lidar waveform consists of a main peak, whose position depends on the target distance and whose amplitude depends on the wavelength of the laser source considered (i.e, on the target reflectivity). Besides, these temporal responses are usually assumed to be corrupted by Poisson noise in the low photon count regime. When considering multiple wavelengths, it becomes possible to use spectral information in order to identify and quantify the main materials in the scene, in addition to estimation of the Lidar-based range profiles. Due to its anomaly detection capability, the proposed hierarchical Bayesian model, coupled with an efficient Markov chain Monte Carlo algorithm, allows robust estimation of depth images together with abundance and outlier maps associated with the observed 3D scene. The proposed methodology is illustrated via experiments conducted with real multispectral Lidar data acquired in a controlled environment. The results demonstrate the possibility to unmix spectral responses constructed from extremely sparse photon counts (less than 10 photons per pixel and band).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Dynamic Mode Decomposition of Random Pressure Fields over Bluff Bodies

Fluctuating surface pressures on a bluff body exposed to a boundary layer flow generally are characterized as a spatiotemporally varying random field. In this paper, a dynamic mode decomposition (DMD) was applied to extract dominant features embedded in these random pressure fields. Utilizing an unsupervised machine learning algorithm, spatial modes and their temporal variations were grouped into different clusters at scales, e.g., macro, meso, and micro. A proper orthogonal decomposition (POD) of the experimental data was carried out to observe commonalities and distinctive perspectives each decomposition offers. Here, a comprehensive examination of the DMD/POD for their convergence criteria, data sufficiency, and modal components analysis was conducted. The physical interpretation of the spatiotemporal pressure field based on these decomposition schemes was discussed. At different scales, the DMD modes can capture the evolution of aerodynamic features, e.g., convection of vortices (or vortex tubes) and other structures. The distribution of energy among these three broad scales also reflects an energy cascade in pressure fluctuations akin to turbulence.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Statistics of Peaks in Chi-Squared Fields

Chi-squared random fields arise naturally from the study of fluctuations in field theories with SO(n) symmetry. The extrema of chi-squared fields are of particular physical interest. In this paper, we undertake a statistical analysis of the stationary points of chi-squared fields, with particular emphasis on extrema. We begin by describing the neighborhood of a stationary point in terms of a biased chi-squared random field, and then compute the expected profile of this field, as well as a variety of associated statistics. We are interested in understanding how spherically symmetric the neighborhood of a stationary point is, on average. To this end, we decompose the biased field into its spherical harmonic modes about this point, and explore their statistics. Using these mode statistics, we construct a metric to gauge the degree of spherical symmetry of the field in this neighborhood. Finally, we show how to leverage the harmonic decomposition to efficiently sample both Gaussian and chi-squared fields about a stationary point.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Random Crystal Field Effect on the Kinetic Spin-1 and Spin-3/2 Under a Time-Dependent Oscillating Field Phase Transitions

The effect of random crystal field on the stationary states of the kinetic spin-1 and spin-3/2 Blume-Capel model is investigated within the framework of the mean field approach. The Glauber-type stochastic dynamics is used to describe the time evolution of the system which is subjected to a time-dependent oscillating external magnetic field. The model exhibits first- and second-order transitions as well as dynamical tricritical, triple and an isolated critical end points. We found that the system displays reentrant phenomenon for both α = 0 and α > 0. Moreover, the system exhibits in the phase space a fixed points and limit cycles with circle, elliptic and parallelogram shapes.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗