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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

(Active) Learning on Groups of Data with Information-Theoretic Estimators

A wide range of machine learning problems, including astronomical inference about galaxy clusters, scene classification, parametric statistical inference, and predictions of public opinion, can be well-modeled as learning a function on (samples from) distributions. This project explores problems in learning such functions via kernel methods, particularly for large-scale problems. When learning from large numbers of distributions, the computation of typical methods scales between quadratically and cubically, and so they are not amenable to large datasets. We investigate the approach of approximate embeddings into Euclidean spaces such that inner products in the embedding space approximate kernel values between the source distributions. We first improve the understanding of the workhorse methods of random Fourier features: we show that of the two approaches in common usage, one is strictly superior. We then present a new embedding for a class of information-theoretic distribution distances, and evaluate it and existing embeddings on several real-world applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2010

Renormalization Group Methods for the NN Interaction in Effective Field Theory

In this work we study the Similarity Renormalization Group (SRG) evolution of an effective nucleon-nucleon (NN) potential obtained by implementing the Subtracted Kernel Method (SKM) approach for the leading-order NN interaction in chiral effective field theory (ChEFT).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Convergence of Weak-SINDy Surrogate Models

In this paper, we give an in-depth error analysis for surrogate models generated by a variant of the Sparse Identification of Nonlinear Dynamics (SINDy) method. We start with an overview of a variety of nonlinear system identification techniques, namely SINDy, weak-SINDy, and the occupation kernel method. Under the assumption that the dynamics are a finite linear combination of a set of basis functions, these methods establish a linear system to recover coefficients. We illuminate the structural similarities between these techniques and establish a projection property for the weak-SINDy technique. Following the overview, we analyze the error of surrogate models generated by a simplified version of weak-SINDy. In particular, under the assumption of boundedness of a composition operator given by the solution, we show that (i) the surrogate dynamics converges towards the true dynamics and (ii) the solution of the surrogate model is reasonably close to the true solution. Finally, as an application, we discuss the use of a combination of weak-SINDy surrogate modeling and proper orthogonal decomposition (POD) to build a surrogate model for partial differential equations (PDEs).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2010

Simulation of the radiation fields from ionizing radiation sources inside the containment in an accident

In the present work, a set of codes used for simulations of the radiation fields from ionizing radiation sources inside the containment in an accident is described. A method of evaluating the gamma dose rate from a space and energy distributed source is given. The dose rate is calculated by means of the design point kernel method and using buildup factors. The code MCU-REA with the ORIMCU module is used for the burnup calculations.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2025

Gaussian Process Regression under Computational and Epistemic Misspecification

Gaussian process regression is a classical kernel method for function estimation and data interpolation. In large data applications, computational costs can be reduced using low-rank or sparse approximations of the kernel. This paper investigates the effect of such kernel approximations on the interpolation error. We introduce a unified framework to analyze Gaussian process regression under important classes of computational misspecification: Karhunen-Loève expansions that result in low-rank kernel approximations, multiscale wavelet expansions that induce sparsity in the covariance matrix, and finite element representations that induce sparsity in the precision matrix. Furthermore, our theory also accounts for epistemic misspecification in the choice of kernel parameters.

Gaussian process regression↗
DOE OSTI2010

Renormalized interaction of relativistic bosons with delta function potentials

We study the interaction of mutually noninteracting Klein-Gordon particles with localized sources on stochastically complete Riemannian surfaces. This asymptotically free theory requires regularization and coupling constant renormalization. Renormalization is performed nonperturbatively using the orthofermion algebra technique and the principal operator {Phi} is found. The principal operator is then used to obtain the bound state spectrum, in terms of binding energies to single Dirac-delta function centers. The heat kernel method allows us to generalize this procedure to compact and Cartan-Hadamard type Riemannian manifolds. We make use of upper and lower bounds on the heat kernel to constrain the ground state energy from below, thus confirming that our neglect of pair creation is justified for certain ranges of parameters in the problem.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Comparison of Zn accumulation and speciation in kernels of sweetcorn and maize differing in maturity

Abstract Background and Aims Understanding the speciation of Zn in edible portions of crops helps identify the most effective biofortification strategies to increase the supply of nutrients for improving the health and nutrition of consumers. Methods Kernels of 12 sweetcorn and three maize (Zea mays) varieties were analysed for Zn concentration and content. The speciation of the Zn in the embryos, endosperms and whole kernels at 21, 28 and 56 days after pollination (DAP) was then examined for one maize and one sweetcorn variety using synchrotron-based X-ray absorption spectroscopy (XAS). Key Results Averaged across all sweetcorn and maize varieties at 21 DAP, the embryo contributed 27–29% of the whole kernel Zn whilst the endosperm contributed 71–73 %. While sweetcorn embryos contributed a lower proportion to the total kernel Zn than those of maize, the proportion of total Zn in the embryo increased as kernels aged for both varieties, reaching 33 % for sweetcorn and 49% for maize at 28 DAP. Using XAS, it was predicted that an average of 90 % of the Zn in the embryos was present as Zn-phytate, while in the endosperm the Zn was primarily complexed with an N-containing ligand such as histidine and to a lesser extent with phytate. However, in maize endosperms, it was also observed that the proportion of Zn present as Zn-phytate increased as the kernel matured, thereby also probably decreasing its bioavailability in these mature maize kernels. Conclusions The apparent low bioavailability of Zn supplied in maize at its consumption stage (i.e. mature kernels) probably undermines the effectiveness of biofortification of this crop. Conversely, successful biofortification of Zn in sweetcorn and green maize consumed as immature kernels could potentially provide a good source of bioavailable Zn in human diets.

Cheah, Zhong Xiang↗
DOE OSTI2021

Evaluation of CloudSat Radiative Kernels Using ARM and CERES Observations and ERA5 Reanalysis

Despite the widespread use of the radiative kernel technique for studying radiative feedbacks and radiative forcings, there has not been any systematic, observation-based validation of the radiative kernel method. Here, we utilize observed and reanalyzed radiative fluxes and atmospheric profiles from the Atmospheric Radiation Measurement (ARM) program and ERA5 reanalysis to assess a set of observation-based radiative kernels from CloudSat for six ARM sites. The CloudSat radiative kernels, convoluted with the ERA5 state variables, can almost perfectly reconstruct the monthly anomalies of shortwave (SW) and longwave (LW) radiative fluxes in ERA5 at the surface (SFC) and top-of-atmosphere (TOA) with correlations significantly being greater than 0.95. The biases of kernel-estimated flux anomalies calculated using the ARM-observed state variables can be more than twice as large when compared with the ARM-observed surface flux anomalies and Clouds and Earth’s Radiant Energy System (CERES) observed anomalies at the TOA. Generally, clouds contribute to most (>60%) of the variance of flux anomalies at Southern Great Plain (SGP), Tropical Western Pacific (TWP), and Eastern North Atlantic (ENA), and surface albedo dominates (>69%) the variance of SW flux anomalies at North Slope of Alaska (NSA). Furthermore, the radiative kernels exhibit the lowest correlation (r ~ [0.55,0.85]) when reconstructing SFC LW flux anomalies at SGP, TWP, and ENA, whose biases are related to the possibility that the kernels may not fully capture the characteristics associated with MJO and ENSO at TWP and the presence of clouds at SGP and ENA.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Recovering missing CFD data for high-order discretizations using deep neural networks and dynamics learning

Data I/O poses a significant bottleneck in large-scale CFD simulations; thus, practitioners would like to significantly reduce the number of times the solution is saved to disk, yet retain the ability to recover any field quantity (at any time instance) a posteriori . The objective of this work is therefore to accurately recover missing CFD data a posteriori at any time instance, given that the solution has been written to disk at only a relatively small number of time instances. We consider in particular high-order discretizations (e.g., discontinuous Galerkin), as such techniques are becoming increasingly popular for the simulation of highly separated flows. To satisfy this objective, this work proposes a methodology consisting of two stages: 1) dimensionality reduction and 2) dynamics learning. For dimensionality reduction, we propose a novel hierarchical approach. First, the method reduces the number of degrees of freedom within each element of the high-order discretization by applying autoencoders from deep learning. Second, the methodology applies principal component analysis to compress the global vector of encodings. This leads to a low-dimensional state, which associates with a nonlinear embedding of the original CFD data. For dynamics learning, we propose to apply regression techniques (e.g., kernel methods) to learn the discrete-time velocity characterizing the time evolution of this low-dimensional state. In conclusion, a numerical example on a large-scale CFD example characterized by nearly 13 million degrees of freedom illustrates the suitability of the proposed method in an industrial setting.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Machine learning of high dimensional data on a noisy quantum processor

Abstract Quantum kernel methods show promise for accelerating data analysis by efficiently learning relationships between input data points that have been encoded into an exponentially large Hilbert space. While this technique has been used successfully in small-scale experiments on synthetic datasets, the practical challenges of scaling to large circuits on noisy hardware have not been thoroughly addressed. Here, we present our findings from experimentally implementing a quantum kernel classifier on real high-dimensional data taken from the domain of cosmology using Google’s universal quantum processor, Sycamore. We construct a circuit ansatz that preserves kernel magnitudes that typically otherwise vanish due to an exponentially growing Hilbert space, and implement error mitigation specific to the task of computing quantum kernels on near-term hardware. Our experiment utilizes 17 qubits to classify uncompressed 67 dimensional data resulting in classification accuracy on a test set that is comparable to noiseless simulation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Higher spin partition functions via the quasinormal mode method in de Sitter quantum gravity

In this note we compute the 1-loop partition function of spin- s s fields on Euclidean de Sitter space S^{2n+1} S 2 n + 1 using the quasinormal mode method. Instead of computing the quasinormal mode frequencies from scratch, we use the analytic continuation prescription L_{\text{AdS}}\to iL_{\text{dS}} L AdS → i L dS , appearing in the dS/CFT correspondence, and Wick rotate the normal mode frequencies of fields on thermal \text{AdS}_{2n+1} AdS 2 n + 1 into the quasinormal mode frequencies of fields on de Sitter space. We compare the quasinormal mode and heat kernel methods of calculating 1-loop determinants, finding exact agreement, and furthermore explicitly relate these methods via a sum over the conformal dimension. We discuss how the Wick rotation of normal modes on thermal \text{AdS}_{2n+1} AdS 2 n + 1 can be generalized to calculating 1-loop partition functions on the thermal spherical quotients S^{2n+1}/\mathbb{Z}_{p} S 2 n + 1 / ℤ p . We further show that the quasinormal mode frequencies encode the group theoretic structure of the spherical spacetimes in question, analogous to the analysis made for thermal AdS in [1-3] .

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Bose-Einstein condensation on a manifold with non-negative Ricci curvature

The Bose-Einstein condensation for an ideal Bose gas and for a dilute weakly interacting Bose gas in a manifold with non-negative Ricci curvature is investigated using the heat kernel and eigenvalue estimates of the Laplace operator. The main focus is on the nonrelativistic gas. However, special relativistic ideal gas is also discussed. The thermodynamic limit of the heat kernel and eigenvalue estimates is taken and the results are used to derive bounds for the depletion coefficient. In the case of a weakly interacting gas, Bogoliubov approximation is employed. The ground state is analyzed using heat kernel methods and finite size effects on the ground state energy are proposed. The justification of the c-number substitution on a manifold is given.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Extensions of BLUP Models for Genomic Prediction in Heterogeneous Populations: Application in a Diverse Switchgrass Sample

Genomic prediction is a useful tool to accelerate genetic gain in selection using DNA marker information. However, this technology typically relies on standard prediction procedures, such as genomic BLUP, that are not designed to accommodate population heterogeneity resulting from differences in marker effects across populations. In this study, we assayed different prediction procedures to capture marker-by-population interactions in genomic prediction models. Prediction procedures included genomic BLUP and two kernel-based extensions of genomic BLUP which explicitly accounted for population heterogeneity. To model population heterogeneity, dissemblance between populations was either depicted by a unique coefficient (as previously reported), or a more flexible function of genetic distance between populations (proposed herein). Models under investigation were applied in a diverse switchgrass sample under two validation schemes: whole-sample calibration, where all individuals except selection candidates are included in the calibration set, and cross-population calibration, where the target population is entirely excluded from the calibration set. First, we showed that using fixed effects, from principal components or putative population groups, appeared detrimental to prediction accuracy, especially in cross-population calibration. Then we showed that modelling population heterogeneity by our proposed procedure resulted in highly significant improvements in model fit. In such cases, gains in accuracy were often positive. These results suggest that population heterogeneity may be parsimoniously captured by kernel methods. However, in cases where improvement in model fit by our proposed procedure is null-to-moderate, ignoring heterogeneity should probably be preferred due to the robustness and simplicity of the standard genomic BLUP model.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Characterization of spent filters from the water polishing system of IEA-R1 Reactor - 15451

The aim of this paper is to report studies performed to determine the activity of gamma emitters present in polypropylene cartridge filters from the polishing system of the IEA-R1 Reactor cooling-water primary circuit. The activities were calculated using the dose rates measured with hand-held detectors, after the ratios of the emission rates of photons were evaluated by gamma spectrometry, and the Point Kernel method, which correlates the activity of a source with the dose rates at various distances. The method described can be used to determine routinely the radioactive inventory of these filters, avoiding the necessity of destructive radiochemical analysis, or the necessity of calibrating the geometry of measurement. (authors)

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2025

Technical note: Recommendations for diagnosing cloud feedbacks and rapid cloud adjustments using cloud radiative kernels

Abstract. The cloud radiative kernel method is a popular approach to quantify cloud feedbacks and rapid cloud adjustments to increased CO2 concentrations and to partition contributions from changes in cloud amount, altitude, and optical depth. However, because this method relies on cloud property histograms derived from passive satellite sensors or produced by passive satellite simulators in models, changes in obscuration of lower-level clouds by upper-level clouds can cause apparent low-cloud feedbacks and adjustments, even in the absence of changes in lower-level cloud properties. Here, we provide a methodology for properly diagnosing the impact of changing obscuration on cloud feedbacks and adjustments and quantify these effects across climate models. Averaged globally and across global climate models, properly accounting for obscuration leads to weaker positive feedbacks from lower-level clouds and stronger positive feedbacks from upper-level clouds while simultaneously removing a mostly artificial anti-correlation between them. Given that the methodology for diagnosing cloud feedbacks and adjustments using cloud radiative kernels has evolved over several papers, and obscuration effects have only occasionally been considered in recent papers, this paper serves to establish recommended best practices and to provide a corresponding code base for community use.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Axion anomalies

We study fermions derivatively coupled to axion-like or pseudoscalar fields, and show that the axial vector current of the fermions is not conserved in the limit where the fermion is massless. This apparent violation of the classical chiral symmetry is due to the background axion field. We compute the contributions to this anomalous Ward identity due to the pseudoscalar field alone, which arise in Minkowski space, as well as the effects due to an interaction with an external gravitational field. For the case of massless fermions, these interactions induce terms in the axion effective action that can be removed by the addition of local counterterms. We demonstrate that these counterterms are generated by the transformation of the path integral measure when transforming the theory from a form where the chiral symmetry is manifest to one where the symmetry is only apparent after using the classical equations of motion. We work perturbatively in Minkowski space and include the effects of interactions with a linearized gravitational field. Using the heat kernel method, we study the transformation properties of the path integral measure, and include the effects of non-linear gravity as well as interactions with gauge fields. Finally, we verify our relation by considering derivatively coupled fermions during pseudoscalar-driven inflation and computing the divergence of the axial current in de Sitter spacetime.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Kohn–Sham accuracy from orbital-free density functional theory via Δ-machine learning

Here, we present a Δ-machine learning model for obtaining Kohn–Sham accuracy from orbital-free density functional theory (DFT) calculations. In particular, we employ a machine-learned force field (MLFF) scheme based on the kernel method to capture the difference between Kohn–Sham and orbital-free DFT energies/forces. We implement this model in the context of on-the-fly molecular dynamics simulations and study its accuracy, performance, and sensitivity to parameters for representative systems. We find that the formalism not only improves the accuracy of Thomas–Fermi–von Weizsäcker orbital-free energies and forces by more than two orders of magnitude but is also more accurate than MLFFs based solely on Kohn–Sham DFT while being more efficient and less sensitive to model parameters. We apply the framework to study the structure of molten Al 0.88 Si 0.12 , the results suggesting no aggregation of Si atoms, in agreement with a previous Kohn–Sham study performed at an order of magnitude smaller length and time scales.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

On-the-fly machine learned force fields for the study of warm dense matter: Application to diffusion and viscosity of CH

We develop a framework for on-the-fly machine learned force field (MLFF) molecular dynamics (MD) simulations of warm dense matter (WDM). In particular, we employ an MLFF scheme based on the kernel method and Bayesian linear regression, with the training data generated from the Kohn–Sham density functional theory (DFT) using the Gauss spectral quadrature method, within which we calculate energies, atomic forces, and stresses. We verify the accuracy of the formalism by comparing the predicted properties of warm dense carbon with recent Kohn–Sham DFT results in the literature. In so doing, we demonstrate that ab initio MD simulations of WDM can be accelerated by up to three orders of magnitude, while retaining ab initio accuracy. We apply this framework to calculate the diffusion coefficients and shear viscosity of CH at a density of 1 g/cm3 and temperatures in the range of 75 000–750 000 K. We find that the self- and inter-diffusion coefficients and the viscosity obey a power law with temperature, and that the diffusion coefficient results suggest a weak coupling between C and H in CH. In addition, we find agreement within standard deviation with previous results for C and CH but disagreement for H, demonstrating the need for ab initio calculations as presented here.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗