SEARCH · Search NASA

Results for “randomized methods”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 451 records · Page 25

All sources NASA NTRS DOE OSTI

NASA NTRS1989

Spectral and correlation analysis with applications to middle-atmosphere radars

The correlation and spectral analysis methods for uniformly sampled stationary random signals, estimation of their spectral moments, and problems arising due to nonstationary are reviewed. Some of these methods are already in routine use in atmospheric radar experiments. Other methods based on the maximum entropy principle and time series models have been used in analyzing data, but are just beginning to receive attention in the analysis of radar signals. These methods are also briefly discussed.

Rastogi, Prabhat K.↗

DOE OSTI2021

Estimating carrying capacity for juvenile salmon using quantile random forest models

Abstract Establishing robust methods and metrics to evaluate habitat quality is critical for the recovery of endangered Pacific salmonids ( Oncorhynchus spp.). A variety of modeling approaches are used for status and trend monitoring of anadromous species throughout the Pacific Northwest, USA, but current methods may fail to capture the complex relationship between fish and habitat and are often limited in predictive power beyond specific watersheds. Further, the focus on species distribution and abundance is not easily manipulated to predict carrying capacity and traditional stock‐recruitment analyses are reliant on long‐term data which are not always available. In this study, we developed a quantile random forest model to provide estimates of habitat carrying capacity for Chinook salmon ( O. tshawytscha ) parr during the summer months, at both the site and watershed scale. Quantile random forest models allow for the consideration of noisy data, correlated variables, and non‐linear relationships: common features in fish–habitat datasets. We leveraged Columbia Habitat Monitoring Program data to select habitat co‐variates and predict capacity at those sites. We also identified a set of globally available attributes to extrapolate capacity estimate predictions throughout wadeable streams within the Columbia River basin. Total capacity estimates for watersheds closely matched estimates from alternative fish productivity models. Carrying capacity estimates based on quantile random forest models, like those presented here, provide managers a framework to guide the identification, prioritization, and development of habitat rehabilitation actions to recover salmon populations.

See, Kevin E.↗

DOE OSTI2021

Synthesis method comparison of compositionally complex rare earth-based Ruddlesden–Popper n = 1 T'-type cuprates

The multicomponent approach has been successfully expanded to the Ruddlesden–Popper structure with the synthesis of two different high-entropy cuprate compositions: (La 0.2 Nd 0.2 Gd 0.2 Tb 0.2 Dy 0.2 ) 2 CuO 4 and (La 0.2 Pr 0.2 Nd 0.2 Sm 0.2 Eu 0.2 ) 2 CuO 4 . Additionally, the effect of synthesis method is explored using both solid-state reaction and polymeric steric entrapment (PSE) methods. It is found that PSE leads to more randomly distributed cation species, providing an advantageous method of synthesis for the growing field of high entropy oxides. In situ high-temperature x-ray diffraction tracks the amorphous to crystalline phase transformation in (La 0.2 Nd 0.2 Gd 0.2 Tb 0.2 Dy 0.2 ) 2 CuO 4 powder, synthesized using the PSE method. Using the High-Temperature XRD data, a method for gaining information on the kinetic behavior is also applied. Magnetometry of both compositions indicates ferrimagnetic behavior at low temperatures.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Isoscalar and isovector giant resonances in 92,94,96,98,100 $\mathrm{Mo}$ and 90,92,94 $\mathrm{Zr}$

The response functions, S(E), and centroid energies, E CEN , of the isoscalar and isovector giant resonances of multipolarity L = 0 - 3 in 92,94,96,98,100 Mo and 90,92,94 Zr have been calculated employing the spherical Hartree-Fock (HF) based random phase approximation (RPA) method, using the Skyrme-type effective nucleon-nucleon interaction. In particular, we investigate the recent experimental results of the uncharacteristic behavior of E CEN for the isoscalar giant resonances, by extending our HF-based RPA calculations with only the KDE0v1 interaction to 32 additional Skyrme interactions. The main result of our investigation is that we find significant disagreements between the theoretical and the experimental values of E CEN for isoscalar giant monopole and dipole resonance of some nuclei. For the isoscalar giant octupole resonance we find the theoretical values of the E CEN to be well above the experimental results. We also study the sensitivity of E CEN to nuclear matter (NM) properties, including the effective mass, the incompressibility coefficient and the symmetry energy at saturation density, by determining the Pearson linear correlation coefficient between the calculated values of E CEN and the various nuclear matter properties of each Skyrme parametrization. So, for the isovector giant dipole resonance we find good agreement between the experimental and theoretical values of E CEN for Skyrme interactions with values of the enhancement coefficient of the energy weighted sum rule, κ, in the range between 0.25 – 0.70

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

Efficient reconstruction and validation of heterogeneous microstructures for energy applications

The digital reconstruction of microstructures is necessary for simulations in fields ranging from geology to electrochemistry, but the state-of-the-art digital reconstruction techniques often compromise between resolution and field of view. It is challenging to retain detailed microstructure information in large-scale reconstructions. Here, this study investigates different aspects of the Yeong-Torquato algorithm based on correlation functions to make it more efficient. We achieve this goal by reducing the computational complexity of the chord-length distribution function and the two-point correlation function, applying the random sphere-packing method as the initial condition, and restricting potential voxel swaps to interfaces. In addition, a novel superposition parallel scheme is introduced to aid in searching for potential voxel swaps. The algorithm proposed is validated by comparing the pore-size distributions of reconstructed 3D custom battery electrodes from a sample dataset obtained from transmission X-ray microscopy. From a sample image with 200 x 200 pixels, the code can reconstruct a 300 x 300 x 300 structure in under 22 h and reconstruct a 400 x 400 x 400 structure in 43 h with eight cores.

42 ENGINEERING↗

DOE OSTI2023

Use of Longitudinal Serum Analysis and Machine Learning to Develop a Classifier for Cancer Early Detection

Early detection of solid tumors through a simple screening process, such as the proteomic analysis of biofluids, has the potential to significantly alter the management and outcomes of cancers. The application of advanced targeted proteomics measurements and data analysis strategies to uniformly collected serum or plasma samples would enable longitudinal studies of cancer risk, progression, and response to therapy that have the potential to significantly reduce cancer burden in general. In this article, we describe a generalizable workflow combining robust, multiplexed targeted proteomics measurements applied to longitudinal samples from the Department of Defense Serum Repository with a Random Forest machine learning method for developing and initially evaluating the performance of candidate biomarker panels for early detection of cancers. The effectiveness of this approach was demonstrated in a cohort of 175 head and neck squamous cell carcinoma patients. The outlined protocols include methods for sample preparation, instrument analysis, and data analysis and interpretation using this workflow.

Longitudinal analysis, machine learning, cancer, e↗

DOE OSTI2023

Solving MaxCut with quantum imaginary time evolution

We introduce a method to solve the MaxCut problem efficiently based on quantum imaginary time evolution (QITE). We employ a linear Ansatz for unitary updates and an initial state involving no entanglement, as well as an imaginary-time-dependent Hamiltonian interpolating between a given graph and a subgraph with two edges excised. We apply the method to thousands of randomly selected graphs with up to fifty vertices. We show that our algorithm exhibits a 93% and above performance converging to the maximum solution of the MaxCut problem for all considered graphs. Our results compare favorably with the performance of classical algorithms, such as the greedy and Goemans–Williamson algorithms. We also discuss the overlap of the final state of the QITE algorithm with the ground state as a performance metric, which is a quantum feature not shared by other classical algorithms.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Simulation of dynamic crystal plasticity with a Lagrangian discontinuous Galerkin hydrodynamic method

Here we present a new Lagrangian modal discontinuous Galerkin (DG) hydrodynamic method that supports a dynamic dislocation based crystal plasticity model for simulating the mechanical behavior of crystallographic materials, both single crystal and polycrystalline, under dynamic conditions. A modal DG approach is used to evolve fields relevant to conservation laws. These fields are approximated by Taylor series polynomials of varying degree. These polynomials describe macro-scale hydrodynamic behavior while their evolution is determined by evaluating the dynamic crystal plasticity model at material points within the element. The dynamic crystal plasticity model is sensitive to the time increment size, with too large of time increments leading to instability in the model. To mitigate this, the temporal evolution of the dynamic crystal plasticity model is achieved with the combination of a sub-incrementing scheme with Heun’s third-order time integration scheme, which is also used to temporally evolve the governing equations. The implementation of the dynamic crystal plasticity model within the DG framework is tested using a 2D approximation of the Taylor impact test with a single crystal material, using quadratic elements that have faces that can bend. In addition to the standard continuous material modeling, we propose a new simulation method that would represent the heterogeneous behavior of polycrystalline microstructures within an element by varying the position and material properties of the material points within the element. This method is demonstrated using random orientation distributions on materials points that are arranged in both structured and random configurations.

42 ENGINEERING↗

DOE OSTI2023

Hierarchical reconstruction of 3D well-connected porous media from 2D exemplars using statistics-informed neural network

The relationships between porous microstructures and transport properties are of fundamental importance in various scientific and engineering applications. Due to the intricacy, stochasticity and heterogeneity of porous media, reliable characterization and modeling of transport properties often require a complete dataset of internal microstructure samples. However, it is often an unbearable cost to acquire sufficient 3D digital microstructures by purely using microscopic imaging systems. Herein this paper presents a machine learning-based technique to hierarchically reconstruct 3D well-connected porous microstructures from one isotropic or several anisotropic low-cost 2D exemplar(s). To compactly characterize the large-scale microstructural features, a Gaussian image pyramid is built for each 2D exemplar. Local morphology patterns are collected from the Gaussian image pyramids, and then they serve as the training data to embed the 2D morphological statistics into feed-forward neural networks at multiple length levels. By using a specially-developed morphology integration scheme, the 3D morphological statistics at different levels can be inferred from the statistics-informed neural networks. Gibbs sampling is adopted to hierarchically reconstruct 3D microstructures by using multi-level 3D morphological statistics, where the large-scale, regional and local morphological patterns are statistically generated and successively added to the same 3D random field. The proposed method is tested on a series of porous media with distinct morphologies, and the statistical equivalence between the reconstructed and the real microstructures is systematically evaluated by comparing morphological descriptors and transport properties. The results demonstrate that the proposed 2D-to-3D microstructure reconstruction method is a universal and efficient approach to generating morphologically and physically realistic samples of porous media.

42 ENGINEERING↗

DOE OSTI2018

A Finite-Field Approach for GW Calculations beyond the Random Phase Approximation

In this work, we describe a finite-field approach to compute density response functions, which allows for efficient G0W0 and G0W0Γ0 calculations beyond the random phase approximation. The method is easily applicable to density functional calculations performed with hybrid functionals. Lastly, we present results for the electronic properties of molecules and solids and we discuss a general scheme to overcome slow convergence of quasiparticle energies obtained from G0W0Γ0 calculations, as a function of the basis set used to represent the dielectric matrix.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Significance of Low‐Velocity Zones on Solute Retention in Rough Fractures

Natural fractures are characterized by high internal heterogeneity. This internal variability is the cause of flow channeling, which in turn leads to contaminant transport taking place primarily along the high-velocity channels. Mass exchange between the high-velocity channels and the low-velocity zones has the potential to enhance contaminant retention, due to solute diffusion into the low-velocity zones and subsequent exposure to additional surface area for diffusion into the bordering rock matrix. Here, we derive a random walk particle tracking method for heterogeneous fractures, which includes an additional term to account for the aperture gradient. The method takes into account advection, diffusion in the fracture and matrix diffusion. The developed numerical framework is applied to assess the effect of low-velocity zones in rough self-affine fractures. The results show that diffusion into low-velocity zones has a visible but modest impact on contaminant retention. The magnitude of this impact does not change considerably, regardless of whether diffusion into the rock matrix is considered in the model, and increases for a decreasing average Péclet number of the fracture.

58 GEOSCIENCES↗

DOE OSTI2024

Analysis of multipacting threshold sensitivity to the random distributions of the secondary electron yield parameters

The way multipacting develops, depends strongly on the secondary emission property of the surface material. The knowledge of secondary electron yield is crucial for accurate prediction of the multipacting threshold. Variations in secondary electron yield parameters from experimental measurements create uncertainty, stemming from handling and surface preparation, and these uncertainties significantly affect multipacting threshold predictions. Despite their significance, the previous studies on the multipacting phenomenon did not adequately address the effect of an assumed random distribution of the secondary emission parameters on the multipacting threshold. Therefore, this paper aims to provide a comprehensive statistical study on how the different random distributions of the secondary emission parameters and, as a result, the uncertainty in the secondary electron yield affect multipacting thresholds. We focus on three commonly used distributions, namely uniform, normal, and truncated normal distributions, to define the uncertainty of random inputs. We use the chaos polynomial expansion method to determine how much each of the random parameters contributes to the multipacting threshold uncertainty. Additionally, we calculate Sobol sensitivity indices to evaluate the impact of the individual parameters or groups of parameters on the model outputs and study how different random distributions of these parameters affected the Sobol index results.

physics↗

DOE OSTI2025

Electric dipole excitations near the neutron separation energies in 96 Mo

Electric dipole strength near the neutron separation energy significantly impacts nuclear structure properties and astrophysical scenarios. These excitations are complex in nature and may involve the so-called pygmy dipole resonance (PDR). Transition densities play a crucial role in understanding the nature of nuclear excited states, including collective excitations, as well as in constructing transition potentials in DWBA or coupled-channels equations. In this work, we focus on electric dipole excitations in spherical molybdenum isotopes, particularly 96 Mo, employing fully consistent Hartree-Fock-Bogoliubov (HFB) and Quasiparticle Random Phase Approximation (QRPA) methods. We analyze the dipole strength near the neutron separation energy, which represents the threshold for neutron capture processes, and examine the isospin characteristics of PDR states through transition density calculations. Examination of proton and neutron transition densities reveals distinctive features of each dipole state, indicating their isoscalar and isovector nature. We observe that the primary component in the enhanced low-energy region exhibits isovector character. The PDR displays a mixture of isoscalar and isovector nature, distinguishing it from the isovector giant dipole resonance (IVGDR). These findings lay the groundwork for future investigations into the role of transition densities in reaction models and for their application to inelastic scattering calculations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Simulating Z 2 lattice gauge theory on a quantum computer

The utility of quantum computers for simulating lattice gauge theories is currently limited by the noisiness of the physical hardware. Various quantum error mitigation strategies exist to reduce the statistical and systematic uncertainties in quantum simulations via improved algorithms and analysis strategies. We perform quantum simulations of Z 2 gauge theory with matter to study the efficacy and interplay of different error mitigation methods: readout error mitigation, randomized compiling, rescaling, and dynamical decoupling. We compute Minkowski correlation functions in this confining gauge theory and extract the mass of the lightest spin-1 state from fits to their time dependence. Quantum error mitigation extends the range of times over which our correlation function calculations are accurate by a factor of 6 and is therefore essential for obtaining reliable masses.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

AGS-GNN: Attribute-guided Sampling for Graph Neural Networks

We propose AGS-GNN, a novel attribute-guided sampling algorithm for Graph Neural Networks (GNNs) that exploits node features and connectivity structure of a graph while simultaneously adapting for both homophily and heterophily in graphs. (In homophilic graphs vertices of the same class are more likely to be connected, and vertices of different classes tend to be linked in heterophilic graphs.) While GNNs have been successfully applied to homophilic graphs, their application to heterophilic graphs remains challenging. The best-performing GNNs for heterophilic graphs do not fit the sampling paradigm, suffer high computational costs, and are not inductive. We employ samplers based on feature-similarity and feature-diversity to select subsets of neighbors for a node, and adaptively capture information from homophilic and heterophilic neighborhoods using dual channels. Currently, AGS-GNN is the only algorithm that we know of that explicitly controls homophily in the sampled subgraph through similar and diverse neighborhood samples. For diverse neighborhood sampling, we employ submodularity, which was not used in this context prior to our work. The sampling distribution is pre-computed and highly parallel, achieving the desired scalability. Using an extensive dataset consisting of 35 small (<=100K nodes) and large (>100K nodes) homophilic and heterophilic graphs, we demonstrate the superiority of AGS-GNN compare to the current approaches in the literature. AGS-GNN achieves comparable test accuracy to the best-performing heterophilic GNNs, even outperforming methods using the entire graph for node classification. AGS-GNN also converges faster compared to methods that sample neighborhoods randomly, and can be incorporated into existing GNN models that employ node or graph sampling.

artificial intelligence↗

DOE OSTI2017

Comparative Analysis and Classification of Cassette Exons and Constitutive Exons

Alternative splicing (AS) is a major engine that drives proteome diversity in mammalian genomes and is a widespread cause of human hereditary diseases. More than 95% of genes in the human genome are alternatively spliced, and the most common type of AS is the cassette exon. Recent discoveries have demonstrated that the cassette exon plays an important role in genetic diseases. To discover the formation mechanism of cassette exon events, we statistically analyze cassette exons and find that cassette exon events are strongly influenced by individual exons that are smaller in size and that have a lower GC content, more codon terminations, and weaker splice sites. We propose an improved random-forest-based hybrid method of distinguishing cassette exons from constitutive exons. Our method achieves a high accuracy in classifying cassette exons and constitutive exons and is verified to outperform previous approaches. It is anticipated that this study will facilitate a better understanding of the underlying mechanisms in cassette exons.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

GANISP (GAN-assisted Importance SPlitting)

Genealogical importance splitting marches towards a rare event by iteratively selecting and replicating realizations that are headed towards a rare event. The replication step is made difficult when applied to deterministic systems as the initial conditions of the offspring realizations need to be adjusted. Typically, a random perturbation is applied to the offspring. For some cases, this cloning technique may not be adequate and prevent variance reduction in the probability estimate. A GAN-based replication process is proposed to address this limitation. The perturbations applied to the clones are physically consistent instead of being randomly chosen. The proposed method allows reducing the variance in the probability estimation.

Hassanaly, Malik↗

DOE OSTI2025

Transcripts and genomic intervals associated with variation in metabolite abundance in maize leaves under field conditions

Abstract Plants exhibit extensive environment-dependent intraspecific metabolic variation, which likely plays a role in determining variation in whole plant phenotypes. However, much of the work seeking to use natural variation to link genes and transcript’s impacts on plant metabolism has employed data from controlled environments. Here, we generated and analyzed data on the variation in the abundance of 26 metabolites across 660 maize inbred lines under field conditions. We employ these data and previously published transcript and whole plant phenotype data reported for the same field experiment to identify both genomic intervals (through genome-wide association studies (GWAS)) and transcripts (using both transcriptome-wide association studies (TWAS) and an explainable artificial intelligence (AI) approach based on random forest (RF)) associated with variation in metabolite abundance. Both genome-wide association and random forest-based methods identified substantial numbers of significant associations including genes with plausible links to the metabolites they are associated with. In contrast, the transcriptome-wide association identified only six significant associations. In three cases, genetic markers associated with metabolic variation in our study colocalized with markers linked to variation in non-metabolic traits scored in the same experiment. We speculate that the poor performance of transcriptome-wide association studies in identifying transcript-metabolite associations may reflect a high prevalence of non-linear interactions between transcripts and metabolites and/or a bias towards rare transcripts playing a large role in determining intraspecific metabolic variation.

Mathivanan, Ramesh Kanna↗