Search NASA⌕ Search

SEARCH · Search NASA

Results for “Model Inference”

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.

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

At least 1,603 records · Page 89
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

The Casualidad method for uncertainty evaluation of best-estimate system thermal-hydraulics calculations

The present paper deals with the description of the salient features of three independent approaches for estimating uncertainties associated with predictions of complex system codes. The first approach is the 'standard' one and the most used at the industrial level: it is based upon the selection of input uncertain parameters, on assigning related ranges of variations and, possibly, PDF (Probability Density Functions) and on performing a suitable number of code runs to get the combined effect of variation on the results. In the second approach the uncertainty derives from the comparison between relevant measured data and results of corresponding code calculations. The third approach is based on the Bayesian inference technique and on the availability of experimental data by which computer model predictions can be improved and the ranges of variation of (in theory) 'all' input parameters can be characterized. More details are provided in respect with the third approach that has been named CASUALIDAD (Code with the capability of Adjoint Sensitivity and Uncertainty Analysis by Internal Data Adjustment and assimilation). (authors)

42 ENGINEERING↗
DOE OSTI2025

Exploring how urban form and demographics are linked with pedestrian and bicycle safety

With pedestrian and bicycle safety as the focus, this study investigates the role of urban form, burdened communities (BCs), and demographics at the national level. Urban form can contribute to segregation, limiting access to crucial resources such as safe infrastructure, essential services, and economic opportunities. Leveraging recent data, this research applies six key indicators to identify BCs based on various socioeconomic and environmental factors. Here, the study creates a unique database combining 10 years of pedestrian-bicycle-involved fatal crashes with data for the 71,729 census tracts with burden indicators and census data. The data are analyzed using descriptives and rigorous zero-hurdle negative binomial models, which account for excessive zeros observed in the data. The inference-based analysis results reveal a positive correlation between burden indicators and pedestrian-bicycle-involved fatal crash occurrences, alongside a heightened risk in areas with high-intensity development. Higher Black, American Indian, or Alaska Native populations are associated with more fatal crashes. The study offers novel insights into safety dynamics across different contexts characterized by urban forms, BCs, and demographics. The study underscores the importance of targeted interventions to enhance pedestrian and bicycle safety.

bicycle crashes↗
DOE OSTI2015

CO-SPATIAL LONG-SLIT UV/OPTIC AL SPECTRA OF 10 GALACTIC PLANETARY NEBULAE WITH HST/STIS. I. DESCRIPTION OF THE OBSERVATIONS, GLOBAL EMISSION-LINE MEASUREMENTS, AND CNO ABUNDANCES

We present observations and initial analysis from a Hubble Space Telescope (HST) Cycle 19 program using STIS to obtain the first co-spatial, UV–optical spectra of 10 Galactic planetary nebulae (PNs). Our primary objective was to measure the critical emission lines of carbon and nitrogen with unprecedented signal-to-noise ratio (S/N) and spatial resolution over the wavelength range 1150–10270 Å, with the ultimate goal of quantifying the production of these elements in low- and intermediate-mass stars. Our sample was selected from PNs with a near-solar metallicity, but spanning a broad range in N/O based on published ground-based and IUE spectra. This study, the first of a series, concentrates on the observations and emission-line measurements obtained by integrating along the entire spatial extent of the slit. We derived ionic and total elemental abundances for the seven PNs with the strongest UV line detections (IC 2165, IC 3568, NGC 2440, NGC 3242, NGC 5315, NGC 5882, and NGC 7662). We compare these new results with other recent studies of the nebulae and discuss the relative merits of deriving the total elemental abundances of C, N, and O using ionization correction factors (ICFs) versus summed abundances. For the seven PNs with the best UV line detections, we conclude that summed abundances from direct diagnostics of ions with measurable UV lines give the most accurate values for the total elemental abundances of C and N (although ICF abundances often produced good results for C). In some cases where significant discrepancies exist between our abundances and those from other studies, we show that the differences can often be attributed to their use of fluxes that are not co-spatial. Finally, we examined C/O and N/O versus O/H and He/H in well-observed Galactic, LMC, and SMC PNs and found that highly accurate abundances are essential for properly inferring elemental yields from their progenitor stars. Future papers will discuss photoionization modeling of our observations, of both the integrated spectra and spatial variations of the UV versus optical lines along the STIS slit lengths, which are unique to our observations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Time-dependent collisional radiative modeling of tungsten in the magnetic sheath for erosion diagnosis

Abstract Tungsten is the material of choice for the divertors in ITER, SPARC and future fusion reactors. Accurate diagnosis of tungsten erosion and migration is important for first wall life time, slag production and core performance. The addition of a magnetic presheath requires time-dependent collisional radiative effects to be included for accurate neutral tungsten collisional radiative modeling. Gross erosion measurements could be modified by a factor of 10 due to the inclusion of time-dependent effects for ITER relevant divertor conditions. A simple sputtering model and sheath density model are developed to investigate time-dependent collisional radiative effects. Neutral tungsten spectral lines populated from different metastable levels depend on model parameters leading to potential spectroscopic diagnostics of plasma parameters. Electron temperatures inferred from spectroscopic line ratios are in agreement with Langmuir probe measurements in the Compact Toroidal Hybrid.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Single-ion properties of the transverse-field Ising model material CoNb 2 O 6

We report CoNb 2 O 6 is one of the few materials that is known to approximate the one-dimensional transverse-field Ising model (1D-TFIM) near its quantum critical point. It has been inferred that Co 2+ acts as a pseudospin 1/2 with anisotropic exchange interactions that are largely Ising-like, enabling the realization of the TFIM. However, the behavior of CoNb 2 O 6 is known to diverge from the ideal TFIM under transverse magnetic fields that are far from the quantum critical point, requiring the consideration of additional anisotropic, bond-dependent (Kitaev-like) terms in the microscopic pseudospin-1/2 Hamiltonian. These terms are expected to be controlled in part by single-ion physics, namely the wave function for the pseudospin-1/2 angular momentum doublet. Here, we present the results of both inelastic neutron scattering measurements and electron paramagnetic resonance spectroscopy on CoNb 2 O 6 , which elucidate the single-ion physics of Co 2+ in CoNb 2 O 6 for the first time. We find that the system is well-described by an intermediate spin-orbit coupled Hamiltonian, and the ground state is a well-isolated Kramers doublet with an anisotropic g tensor. We provide the approximate wave functions for this doublet, which we expect will be useful in theoretical investigations of the anisotropic exchange interactions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Inference for Nonparanormal Partial Correlation via Regularized Rank-Based Nodewise Regression

Abstract Partial correlation is a common tool in studying conditional dependence for Gaussian distributed data. However, partial correlation being zero may not be equivalent to conditional independence under non-Gaussian distributions. In this paper, we propose a statistical inference procedure for partial correlations under the high-dimensional nonparanormal (NPN) model where the observed data are normally distributed after certain monotone transformations. The NPN partial correlation is the partial correlation of the normal transformed data under the NPN model, which is a more general measure of conditional dependence. We estimate the NPN partial correlations by regularized nodewise regression based on the empirical ranks of the original data. A multiple testing procedure is proposed to identify the nonzero NPN partial correlations. The proposed method can be carried out by a simple coordinate descent algorithm for lasso optimization. It is easy-to-implement and computationally more efficient compared to the existing methods for estimating NPN graphical models. Theoretical results are developed to show the asymptotic normality of the proposed estimator and to justify the proposed multiple testing procedure. Numerical simulations and a case study on brain imaging data demonstrate the utility of the proposed procedure and evaluate its performance compared to the existing methods. Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Particle Filter Based Inference Testing

The primary intent of PAR-FIT (Particle Filter based Inference Testing) is to provide hard inductive evidence that a machine learning model is capable and proven for an individual test input. By examining training data used to form the underlying model functional correlation, an estimate of the reliability that a model will make the correct prediction can be made. The Sequential Probability Ratio Test is used to derive a qualitative evaluation for reliability based on hypothesis testing. The PAR-FIT framework achieves this by implementing a particle filter and the sequential probability ratio test algorithms on the machine learning model training data to determine relevancy of new individual test samples to the training dataset. The kernel function evaluates the local proximity and density of training data used to derive a prediction outcome. Particles are used to probabilistically determine which training data to evaluate for proximity. For test samples that are within a close proximity to and surrounded by multiple training data points, the evaluated reliability of the prediction is high. For test samples that are anomalies not represented by the training dataset, in low density data clusters, or are far from existing data points, the evaluated reliability is low as insufficient training evidence exists to suggest the model is capable of making the correct prediction. Sequential Probability Ratio Test is further used to determine when a hypothesis on whether a signal can be rejected or accepted for use. The ratio test collects sequence information from the particle filter to test whether the signal is anomalous or normal via hypothesis testing of the underlying distributions.

Chen, Edward [Idaho National Laboratory (INL), Ida↗
DOE OSTI2020

NEON AOP foliar trait maps, maps of model uncertainty estimates, and conifer map, East River, CO 2018

This data package contains mapped trait estimates and their uncertainties, and conifer map, for the National Ecological Observatory Network's Airborne Observation Platform survey data acquired over the Upper East River, Colorado in 2018. For full details, please see associated reference. in brief, trait models were developed independently for needle and non-needle leaf species using partial least squares regression (PLSR) using ground data from additional datasets: doi:10.15485/1618130, doi:10.15485/1618132, and doi:10.15485/1631278, merged with extracted spectral data from doi:10.15485/1618131. We separated vegetated pixels into needle and non-needle classes in order to generate a classification map based on the spectral differences between these leaf types (conifer.tif). We trained a deep learning model with custom architecture, detailed in Chadwick et al. In Press. The model performed with 0.998 true positive rate and 0.982 true negative rate, with ‘positives’ being non-needle identification. We then utilized PLSR to generate models of foliar traits for each leaf type. So that we could also map uncertainty in these predictions, we generated ten different models for needle and non-needle leaf species using different testing holdout sets of discrete sites. Each of these models was developed with a 100-fold cross validation procedure that utilized a 70% training set and 30% validation set with each fold, and then assessed based on the 10% of testing sites that were not included in that model’s development. The mean predicted value across the 10 models is used for the trait estimate in each pixel across the study area. The models are applied according to the leaf type designation in the conifer.tif map. The errors are the standard deviation across the 10 different models developed, with high error suggesting instability in model prediction and areas where values may not be reliable for ecological inference. These maps are only applied to areas with a NDVI > 0.5 to exclude non-vegetated areas. Shade masks could be applied to these data (doi:10.15485/1618131), but have not been for this data package. These data are also available on Google Earth Engine: https://code.earthengine.google.com/?asset=users/kdc/ER_NEON

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Basin-Scale Structural Features Database

The Basin-Scale Structural Features database provides spatial datasets of faults, fractures, folds, and earthquakes compiled from public, authoritative sources (e.g., U.S. Geological Survey and State Geological Surveys) and aggregated into derivative forms to support subsurface assessments. Recognizing that characterizing basin-scale structural features requires interpreting data that are often ambiguous or lack key information, the source data were evaluated using a knowledge-data framework and geospatial fuzzy logic method (Justman et al., 2020) to represent both measured (observed) and predicted (inferred or potential) structural features as derivative datasets. This workflow employs conceptual models for known structural features and predicted structural features, incorporating geospatial data to estimate potential, even with limited data. The aim is to aid and support an understanding of basin-scale features and identify potential gaps in data and knowledge. As of 4/30/2025, the database includes resources for nine sedimentary basins: Appalachian, Denver, U.S. Gulf Coast, Illinois, Michigan, Permian, Sacramento, San Joquin and Williston. The database is organized by basin and then data category: 1) Faults, fractures, folds, 2) Earthquakes, 3) Topographic, 4) Structural contours and isopachs, 5) Geophysical, and 6) Structural feature density assessment maps.

basin scale↗
DOE OSTI2015

Novel Sensor for the In Situ Measurement of Uranium Fluxes

The goal of this project was to develop a sensor that incorporates the field-tested concepts of the passive flux meter to provide direct in situ measures of flux for uranium and groundwater in porous media. Measurable contaminant fluxes [J] are essentially the product of concentration [C] and groundwater flux or specific discharge [q ]. The sensor measures [J] and [q] by changes in contaminant and tracer amounts respectively on a sorbent. By using measurement rather than inference from static parameters, the sensor can directly advance conceptual and computational models for field scale simulations. The sensor was deployed in conjunction with DOE in obtaining field-scale quantification of subsurface processes affecting uranium transport (e.g., advection) and transformation (e.g., uranium attenuation) at the Rifle IFRC Site in Rifle, Colorado. Project results have expanded our current understanding of how field-scale spatial variations in fluxes of uranium, groundwater and salient electron donor/acceptors are coupled to spatial variations in measured microbial biomass/community composition, effective field-scale uranium mass balances, attenuation, and stability. The coupling between uranium, various nutrients and micro flora can be used to estimate field-scale rates of uranium attenuation and field-scale transitions in microbial communities. This research focuses on uranium (VI), but the sensor principles and design are applicable to field-scale fate and transport of other radionuclides. Laboratory studies focused on sorbent selection and calibration, along with sensor development and validation under controlled conditions. Field studies were conducted at the Rifle IFRC Site in Rifle, Colorado. These studies were closely coordinated with existing SBR (formerly ERSP) projects to complement data collection. Small field tests were conducted during the first two years that focused on evaluating field-scale deployment procedures and validating sensor performance under controlled field conditions. In the third and fourth year a suite of larger field studies were conducted. For these studies, the uranium flux sensor was used with uranium speciation measurements and molecular-biological tools to characterize microbial community and active biomass at synonymous wells distributed in a large grid. These field efforts quantified spatial changes in uranium flux and field-scale rates of uranium attenuation (ambient and stimulated), uranium stability, and quantitatively assessed how fluxes and effective reaction rates were coupled to spatial variations in microbial community and active biomass. Analyses of data from these field experiments were used to generate estimates of Monod kinetic parameters that are ‘effective’ in nature and optimal for modeling uranium fate and transport at the field-scale. This project provided the opportunity to develop the first sensor that provides direct measures of both uranium (VI) and groundwater flux. A multidisciplinary team was assembled to include two geochemists, a microbiologist, and two quantitative contaminant hydrologists. Now that the project is complete, the sensor can be deployed at DOE sites to evaluate field-scale uranium attenuation, source behavior, the efficacy of remediation, and off-site risk. Because the sensor requires no power, it can be deployed at remote sites for periods of days to months. The fundamental science derived from this project can be used to advance the development of predictive models for various transport and attenuation processes in aquifers. Proper development of these models is critical for long-term stewardship of contaminated sites in the context of predicting uranium source behavior, remediation performance, and off-site risk.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Behavioral Responses Of Fish To A Current-Based Hydrokinetic Turbine Under Mutlipe Operational Conditions: Final Report

There is significant interest in the interaction of aquatic organisms with current-based marine and hydrokinetic (MHK) technologies. Determining the potential impacts of MHK devices on fish behavior is critical to addressing the environmental concerns that could act as barriers to the permitting and deployment of MHK devices. To address these concerns, we use field monitoring and fish behavior models to characterize the behavioral responses of fish to MHK turbines and infer potential stimuli that may have elicited the observed behavioral changes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

UQTk (V. 3.0) User Manual

The UQ Toolkit (UQTk) is a collection of libraries and tools for the quantification of uncertainty in numerical model predictions. Version 3.0 offers intrusive and non-intrusive methods for propagating input uncertainties through computational models, tools for sensitivity analysis, methods for sparse surrogate construction, and Bayesian inference tools for inferring parameters from experimental data. This manual discusses the download and installation process for UQTk, provides pointers to the UQ methods used in the toolkit, and describes some of the examples provided with the toolkit.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

UQTk User Manual (V.3.1.0)

The UQ Toolkit (UQTk) is a collection of libraries and tools for the quantification of uncertainty in numerical model predictions. Version 3.1.0 offers intrusive and non-intrusive methods for propagating input uncertainties through computational models, tools for sensitivity analysis, methods for sparse surrogate construction, and Bayesian inference tools for inferring parameters from experimental data. This manual discusses the download and installation process for UQTk, provides pointers to the UQ methods used in the toolkit, and describes some of the examples provided with the toolkit.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

UQTk Version 3.1.1 User Manual

The UQ Toolkit (UQTk) is a collection of libraries and tools for the quantification of uncertainty in numerical model predictions. Version 3.1.1 offers intrusive and non-intrusive methods for propagating input uncertainties through computational models, tools for sensitivity analysis, methods for sparse surrogate construction, and Bayesian inference tools for inferring parameters from experimental data. This manual discusses the download and installation process for UQTk, provides pointers to the UQ methods used in the toolkit, and describes some of the examples provided with the toolkit.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

UQTk Version 3.0.4 User Manual

The UQ Toolkit (UQTk) is a collection of libraries and tools for the quantification of uncertainty in numerical model predictions. Version 3.0.4 offers intrusive and non-intrusive methods for propagating input uncertainties through computational models, tools for sensitivity analysis, methods for sparse surrogate construction, and Bayesian inference tools for inferring parameters from experimental data. This manual discusses the download and installation process for UQTk, provides pointers to the UQ methods used in the toolkit, and describes some of the examples provided with the toolkit.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Penetrating power in radiography of high-Z materials as a function of bremsstrahlung spectrum endpoint energy

A modified radiographer’s equation is derived and fitted to simulation data so as to describe the increase in radiographic signal that comes with increasing bremsstrahlung spectrum endpoint energy. The dose contained in the incident spectrum follows an E28 power law, as often cited. However, the direct flux penetrating through a heavy, high-Z material, obeys a weaker scaling due to hardening of the radiation. The resultant expression depends on the thickness of the scene being radiographed, the composition of high-Z materials in the scene, and any filtering by other low-Z materials, such as Al or Be. The expression exhibits two asymptotic limits: a ‘thick’ limit at which radiation is extremely hardened and only a weak benefit to increasing endpoint energy is observed (also the most attenuating limit and therefore of dubious physical significance), and a ‘thin’ limit at which a minimum of high-Z material is present and the penetrating dose exhibits the 2.8 power scaling inherent in the incident spectrum. Behavior for intermediate thicknesses of W and Al are obtained by modeling incident and attenuated dose using the DOSECALC code and the Bayes’ Inference Engine (BIE). These data guide the optimization of MeV radiography over a wide range of energies where pair production plays a significant role in photon scattering.

36 MATERIALS SCIENCE↗
DOE OSTI2022

UQTk Version 3.1.2 User Manual

The UQ Toolkit (UQTk) is a collection of libraries and tools for the quantification of uncertainty in numerical model predictions. Version 3.1.2 offers intrusive and non-intrusive methods for propagating input uncertainties through computational models, tools for sensitivity analysis, methods for sparse surrogate construction, and Bayesian inference tools for inferring parameters from experimental data. This manual discusses the download and installation process for UQTk, provides pointers to the UQ methods used in the toolkit, and describes some of the examples provided with the toolkit.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Digital Signal Processing Using Deep Neural Networks

Currently there is great interest in the utility of deep neural networks (DNNs) for the physical layer of radio frequency (RF) communications. In this manuscript we describe a custom DNN specially designed to solve problems in the RF domain. Our model leverages the mechanisms of feature extraction and attention through the combination of an autoencoder convolutional network with a transformer network, to accomplish several important communications network and digital signals processing (DSP) tasks. We also present a new open dataset and physical data augmentation model that enables training of DNNs that can perform automatic modulation classification, infer, and correct transmission channel effects, and directly demodulate baseband RF signals.

42 ENGINEERING↗