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Results for “statistical 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.

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At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Phase Diagram of a Deep Potential Water Model

Using the Deep Potential methodology, we construct a model that reproduces accurately the potential energy surface of the SCAN approximation of density functional theory for water, from low temperature and pressure to about 2400 K and 50 GPa, excluding the vapor stability region. The computational efficiency of the model makes it possible to predict its phase diagram using molecular dynamics. Satisfactory overall agreement with experimental results is obtained. Here, the fluid phases, molecular and ionic, and all the stable ice polymorphs, ordered and disordered, are predicted correctly, with the exception of ice III and XV that are stable in experiments, but metastable in the model. The evolution of the atomic dynamics upon heating, as ice VII transforms first into ice VII" and then into an ionic fluid, reveals that molecular dissociation and breaking of the ice rules coexist with strong covalent fluctuations, explaining why only partial ionization was inferred in experiments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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↗
NASA NTRS1984

On the X-ray spectrum of the volume emissivity arising from Abell clusters

HEAO 1 A-2 X-ray spectra (2-15 keV) for an optically selected sample of Abell clusters of galaxies with z less than 0.1 have been analyzed to determine the energy dependence of the cosmological X-ray volume emissivity arising from such clusters. This spectrum is well fitted by an isothermal-bremsstrahlung model with kT = 7.4 + or - 1.5 KeV. This result is a test of the isothermal-volume-emissivity spectrum to be inferred from the conjecture that all contributing clusters may be characterized by kT = 7 keV, as assumed by McKee et al. (1980) in estimating the underlying luminosity function for the same sample. Although satisfied at the statistical level indicated, the analysis of a low-luminosity subsample suggests that this assumption of identical isothermal spectra would lead to a systematic error for a more statistically precise determination of the luminosity function's form.

Stottlemyer, A. R.↗
NASA NTRS1989

Neutrinos from SN 1987A - Implications for cooling of the nascent neutron star and the mass of the electron antineutrino

Data on neutrinos from SN 1987A are compared here with parameterized models of the neutrino emission using a consistent and straightforward statistical methodology. The empirically measured detector background spectra are included in the analysis, and the data are compared with a much wider variety of neutrino emission models than was explored previously. It is shown that the inferred neutrino emission model parameters are strongly correlated. The analysis confirms that simple models of the neutrino cooling of the nascent neutron star formed by the SN adequately explain the data. The inferred radius and binding energy of the neutron star are in excellent agreement with model calculations based on a wide range of equations of state. The results also raise the upper limit of the electron antineutrino rest mass to roughly 25 eV at the 95 percent confidence level, roughly 1.5-5 times higher than found previously.

Loredo, Thomas J.↗
NASA NTRS2008

On the Period-Amplitude and Amplitude-Period Relationships

Examined are Period-Amplitude and Amplitude-Period relationships based on the cyclic behavior of the 12-month moving averages of monthly mean sunspot numbers for cycles 0.23, both in terms of Fisher's exact tests for 2x2 contingency tables and linear regression analyses. Concerning the Period-Amplitude relationship (same cycle), because cycle 23's maximum amplitude is known to be 120.8, the inferred regressions (90-percent prediction intervals) suggest that its period will be 131 +/- 24 months (using all cycles) or 131 +/- 18 months (ignoring cycles 2 and 4, which have the extremes of period, 108 and 164 months, respectively). Because cycle 23 has already persisted for 142 months (May 1996 through February 2008), based on the latter prediction, it should end before September 2008. Concerning the Amplitude-Period relationship (following cycle maximum amplitude versus preceding cycle period), because cycle 23's period is known to be at least 142 months, the inferred regressions (90-percent prediction intervals) suggest that cycle 24's maximum amplitude will be about less than or equal to 96.1 +/- 55.0 (using all cycle pairs) or less than or equal to 91.0 +/- 36.7 (ignoring statistical outlier cycle pairs). Hence, cycle 24's maximum amplitude is expected to be less than 151, perhaps even less than 128, unless cycle pair 23/24 proves to be a statistical outlier.

Wilson, Robert M.↗
NASA NTRS2022

Canadian and Alaskan Wildfire Smoke Particle Properties, Their Evolution and Controlling Factors, From Satellite Observations

The optical and chemical properties of biomass burning (BB) smoke particles greatly affect the impact that wildfires have on climate and air quality. Previous work has demonstrated some links between smoke properties and factors such as fuel type and meteorology. However, the factors controlling BB particle speciation at emission are not adequately understood nor are the factors driving particle aging during atmospheric transport. As such, modeling wildfire smoke impacts on climate and air quality remains challenging. The potential to provide robust, statistical characterizations of BB particles based on ecosystem type and ambient environmental conditions with remote sensing data is investigated here. Space-based Multi-angle Imaging SpectroRadiometer (MISR) observations, combined with the MISR Research Aerosol (RA) algorithm and the MISR Interactive Explorer (MINX) tool, are used to retrieve smoke plume aerosol optical depth (AOD) and to provide constraints on plume vertical extent; smoke age; and particle size, shape, light-absorption properties, and absorption spectral dependence. These tools are applied to numerous wildfire plumes in Canada and Alaska, across a range of conditions, to create a regional inventory of BB particle-type temporal and spatial distribution. We then statistically compare these results with satellite measurements of fire radiative power (FRP) and land cover characteristics, as well as short-term climate, meteorological, and drought information from the Modern-Era Retrospective analysis for Research and Applications (MERRA-2) reanalysis and the North American Drought Monitor. We find statistically significant differences in the retrieved smoke properties based on land cover type, with fires in forests producing the thickest plumes containing the largest, brightest particles and fires in savannas and grasslands exhibiting the opposite. Additionally, the inferred dominant aging mechanisms and the timescales over which they occur vary systematically between land types. This work demonstrates the potential of remote sensing to constrain BB particle properties and the mechanisms governing their evolution over entire ecosystems. It also begins to realize this potential, as a means of improving regional and global climate and air quality modeling in a rapidly changing world.

Katherine T. Junghenn Noyes↗
DOE OSTI2021

Large-scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant

We investigate the use of approximate Bayesian neural networks (BNNs) in modeling hundreds of time delay gravitational lenses for Hubble constant (H 0 ) determination. Here, our BNN was trained on synthetic Hubble Space Telescope quality images of strongly lensed active galactic nuclei with lens galaxy light included. The BNN can accurately characterize the posterior probability density functions (PDFs) of model parameters governing the elliptical power-law mass profile in an external shear field. We then propagate the BNN-inferred posterior PDFs into an ensemble H 0 inference, using simulated time delay measurements from a plausible dedicated monitoring campaign. Assuming well-measured time delays and a reasonable set of priors on the environment of the lens, we achieve a median precision of 9.3% per lens in the inferred H 0 . A simple combination of a set of 200 test lenses results in a precision of 0.5 km s –1 Mpc –1 (0.7%), with no detectable bias in this H 0 recovery test. The computation time for the entire pipeline—including the generation of the training set, BNN training and H0 inference—translates to 9 minutes per lens on average for 200 lenses and converges to 6 minutes per lens as the sample size is increased. Being fully automated and efficient, our pipeline is a promising tool for exploring ensemble-level systematics in lens modeling for H 0 inference.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Impacts of floating-point non-associativity on reproducibility for HPC and deep learning applications

Run to run variability in parallel programs caused by floating-point non-associativity has been known to significantly affect reproducibility in iterative algorithms, due to accumulating errors. Non-reproducibility can critically affect the efficiency and effectiveness of correctness testing for stochastic programs. Recently, the sensitivity of deep learning training and inference pipelines to floating-point non-associativity has been found to sometimes be extreme. It can prevent certification for commercial applications, accurate assessment of robustness and sensitivity, and bug detection. New approaches in scientific computing applications have coupled deep learning models with high-performance computing, leading to an aggravation of debugging and testing challenges. Here we perform an investigation of the statistical properties of floating-point non-associativity within modern parallel programming models, and analyze performance and productivity impacts of replacing atomic operations with deterministic alternatives on GPUs. We examine the recently-added deterministic options in PyTorch within the context of GPU deployment for deep learning, uncovering and quantifying the impacts of input parameters triggering run to run variability and reporting on the reliability and completeness of the documentation. Finally, we evaluate the strategy of exploiting automatic determinism that could be provided by deterministic hardware, using the Groq LPUTM accelerator for inference portions of the deep learning pipeline. We demonstrate the benefits that a hardware-based strategy can provide within reproducibility and correctness efforts.

Shanmugavelu, Sanjif↗
DOE OSTI2022

SBbadger: biochemical reaction networks with definable degree distributions

Abstract Motivation An essential step in developing computational tools for the inference, optimization and simulation of biochemical reaction networks is gauging tool performance against earlier efforts using an appropriate set of benchmarks. General strategies for the assembly of benchmark models include collection from the literature, creation via subnetwork extraction and de novo generation. However, with respect to biochemical reaction networks, these approaches and their associated tools are either poorly suited to generate models that reflect the wide range of properties found in natural biochemical networks or to do so in numbers that enable rigorous statistical analysis. Results In this work, we present SBbadger, a python-based software tool for the generation of synthetic biochemical reaction or metabolic networks with user-defined degree distributions, multiple available kinetic formalisms and a host of other definable properties. SBbadger thus enables the creation of benchmark model sets that reflect properties of biological systems and generate the kinetics and model structures typically targeted by computational analysis and inference software. Here, we detail the computational and algorithmic workflow of SBbadger, demonstrate its performance under various settings, provide sample outputs and compare it to currently available biochemical reaction network generation software. Availability and implementation SBbadger is implemented in Python and is freely available at https://github.com/sys-bio/SBbadger and via PyPI at https://pypi.org/project/SBbadger/. Documentation can be found at https://SBbadger.readthedocs.io. Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Statistically-informed deep learning for gravitational wave parameter estimation

We introduce deep learning models to estimate the masses of the binary components of black hole mergers, $(m_1,m_2)$, and three astrophysical properties of the post-merger compact remnant, namely, the final spin, $a_\mathrm f$, and the frequency and damping time of the ringdown oscillations of the fundamental $\ell = m = 2$ bar mode, $(\omega_\mathrm R, \omega_\mathrm I)$. Our neural networks combine a modified WaveNet architecture with contrastive learning and normalizing flow. We validate these models against a Gaussian conjugate prior family whose posterior distribution is described by a closed analytical expression. Upon confirming that our models produce statistically consistent results, we used them to estimate the astrophysical parameters $(m_1,m_2, a_\mathrm f, \omega_\mathrm R, \omega_\mathrm I)$ of five binary black holes: GW150914, GW170104, GW170814, GW190521 and GW190630. We use PyCBC Inference to directly compare traditional Bayesian methodologies for parameter estimation with our deep learning based posterior distributions. Our results show that our neural network models predict posterior distributions that encode physical correlations, and that our data-driven median results and 90% confidence intervals are similar to those produced with gravitational wave Bayesian analyses. This methodology requires a single V100 NVIDIA GPU to produce median values and posterior distributions within two milliseconds for each event. Furthermore, this neural network, and a tutorial for its use, are available at the Data and Learning Hub for Science.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1992

Motion at the ionization front in the Orion nebula - A kinematic study of the forbidden O I line

The kinematics of the ionization front in H II regions is investigated via a study of the collisionally excited forbidden O I emission at 630 nm. Slit spectra of 5/km s resolution are used to examine the velocities of forbidden O I across the inner few arcminutes of M42. The mean velocity is significantly more positive than for other ions, which confirms the argument that the ionization front is formed against the parent molecular cloud OMC 1. It is inferred that there is acceleration of material in the ionization front at the parent molecular cloud. A new model for M42 based largely on optical emission-line and radio absorption-line studies is presented. A detailed statistical analysis indicates agreement between the structure function and the Kolmogorov theory for turbulence.

O'Dell, C. R.↗
DOE OSTI2020

The influence of environmental microseismicity on detection and interpretation of small-magnitude events in a polar glacier setting

Glacial environments exhibit temporally variable microseismicity. To investigate how microseismicity influences event detection, we implement two noise-adaptive digital power detectors to process seismic data from Taylor Glacier, Antarctica. We add scaled icequake waveforms to the original data stream, run detectors on the hybrid data stream to estimate reliable detection magnitudes and compare analytical magnitudes predicted from an ice crack source model. We find that detection capability is influenced by environmental microseismicity for seismic events with source size comparable to thermal penetration depths. When event counts and minimum detectable event sizes change in the same direction (i.e. increase in event counts and minimum detectable event size), we interpret measured seismicity changes as ‘true’ seismicity changes rather than as changes in detection. Generally, one detector (two degree of freedom (2dof)) outperforms the other: it identifies more events, a more prominent summertime diurnal signal and maintains a higher detection capability. We conclude that real physical processes are responsible for the summertime diurnal inter-detector difference. One detector (3dof) identifies this process as environmental microseismicity; the other detector (2dof) identifies it as elevated waveform activity. Our analysis provides an example for minimizing detection biases and estimating source sizes when interpreting temporal seismicity patterns to better infer glacial seismogenic processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Identifying neutron sources using recoil and time-of-flight spectroscopy

Identification of neutron sources is central to nuclear physics and its applications, from planetary science to nuclear security, yet direct source discrimination from measured neutron spectra remains fundamentally elusive. Here, we introduce a Bayesian protocol that directly infers source ensembles from measured neutron spectra by combining full-spectrum template matching with probabilistic evidence evaluation. Applying this protocol to recoil and time-of-flight spectroscopy, we recover single- and two-source configurations with strong statistical significance (beyond 4⁢𝜎) at event counts as low as ∼10 3 . These results demonstrate that neutron spectral signatures can be leveraged for robust source identification, opening a new observational window for both fundamental research and operationally driven applications.

neutron physics↗
DOE OSTI2026

Data-driven particle dynamics: Structure-preserving coarse-graining for emergent behavior in non-equilibrium systems

Multiscale systems are ubiquitous in science and technology, but are notoriously challenging to simulate as short spatiotemporal scales must be appropriately linked to emergent bulk physics. When expensive high-dimensional dynamical systems are coarse-grained into low-dimensional models, the entropic loss of information leads to emergent physics which are dissipative, history-dependent, and stochastic. To machine learn coarse-grained dynamics from time-series observations of particle trajectories, we propose a framework using the metriplectic bracket formalism that preserves these properties by construction; most notably, the framework guarantees discrete notions of the first and second laws of thermodynamics, conservation of momentum, and a discrete fluctuation-dissipation balance crucial for capturing non-equilibrium statistics. We introduce the mathematical framework abstractly before specializing to a particle discretization. As labels are generally unavailable for entropic state variables, we introduce a novel self-supervised learning strategy to identify emergent structural variables. We validate the method on benchmark systems and demonstrate its utility on two challenging examples: (1) coarse-graining star polymers at challenging levels of coarse-graining while preserving non-equilibrium statistics, and (2) learning models from high-speed video of colloidal suspensions that capture coupling between local rearrangement events and emergent stochastic dynamics. We provide open-source implementations in both PyTorch and LAMMPS, enabling large-scale inference and extensibility to diverse particle-based systems.

Computational Engineering, Finance, and Science (c↗
NASA NTRS1978

Manicouagan impact melt, Quebec. I - Stratigraphy, petrology, and chemistry

A sheet of clast-laden impact melt 230 m thick and 55 km in diameter forms an annular plateau surrounding an uplift of shocked anorthosite within the moderately eroded Manicouagan structure. Three gradational units of the melt sheet are characterized with respect to grain size, inclusions, texture, and mineralogy. The melt rocks as a group are chemically homogeneous with a bulk composition similar to that of latite and with no statistically significant regional chemical variations. The melt is not completely chemically homogeneous as a local mafic variant represented by two samples with poikilitic texture was found. These poikilitic rocks texturally resemble some Apollo 17 impact melt rocks and are inferred to have had a similar origin and thermal history.

Floran, R. J.↗
NASA NTRS2016

Optical and Near-Infrared Observations of SN 2013DX Associated with GRB 130702A

We present optical and near-infrared (NIR) light curves and optical spectra of SN 2013dx, associated with the nearby (redshift 0.145) gamma-ray burst GRB 130702A. The prompt isotropic gamma-ray energy released from GRB 130702A is measured to be E(sub gamma, iso) = 6.4(+1.3/-1.0) x 10(exp 50) erg (1 keV to 10 MeV in the rest frame), placing it intermediate between low-luminosity GRBs like GRB 980425/SN 1998bw and the broader cosmological population. We compare the observed g'r'i'z' light curves of SN 2013dx to a SN 1998bw template, finding that SN 2013dx evolves approx. 20% faster (steeper rise time), with a comparable peak luminosity. Spectroscopically, SN 2013dx resembles other broad-lined SNe Ic, both associated with (SN 2006aj and SN 1998bw) and lacking (SN 1997ef, SN 2007I, and SN 2010ah) gamma-ray emission, with photospheric velocities around peak of approx. 21,000 km/s. We construct a quasi-bolometric (g'r'z'yJ) light curve for SN 2013dx, only the fifth GRB-associated SN with extensive NIR coverage and the third with a bolometric light curve extending beyond (Delta)t > 40 days. Together with the measured photospheric velocity, we derive basic explosion parameters using simple analytic models. We infer a Ni-56 mass of M(sub Ni) = 0.37+/- 0.01 Stellar Mass, an ejecta mass of M(sub ej) = 3.1+/- 0.1 Stellar Mass, and a kinetic energy of E(sub K) = (8.2+/- 0.43) x 10(exp 51) erg (statistical uncertainties only), consistent with previous GRB-associated supernovae. When considering the ensemble population of GRB-associated supernovae, we find no correlation between the mass of synthesized Ni-56 and high-energy properties, despite clear predictions from numerical simulations that M(sub Ni) should correlate with the degree of asymmetry. On the other hand, M(sub Ni) clearly correlates with the kinetic energy of the supernova ejecta across a wide range of core-collapse events.

gamma-ray burst: individual (GRB 130702A) – supe↗
NASA NTRS1998

The Use of a Satellite Climatological Data Set to Infer Large Scale Three Dimensional Flow Characteristics

Ever since the first satellite image loops from the 6.3 micron water vapor channel on the METEOSAT-1 in 1978, there have been numerous efforts (many to a great degree of success) to relate the water vapor radiance patterns to familiar atmospheric dynamic quantities. The realization of these efforts is becoming evident with the merging of satellite derived winds into predictive models (Velden et al., 1997; Swadley and Goerss, 1989). Another parameter that has been quantified from satellite water vapor channel measurements is upper tropospheric relative humidity (UTH) (e.g., Soden and Bretherton, 1996; Schmetz and Turpeinen, 1988). These humidity measurements, in turn, can be used to quantify upper tropospheric water vapor and its transport to more accurately diagnose climate changes (Lerner et al., 1998; Schmetz et al. 1995a) and quantify radiative processes in the upper troposphere. Also apparent in water vapor imagery animations are regions of subsiding and ascending air flow. Indeed, a component of the translated motions we observe are due to vertical velocities. The few attempts at exploiting this information have been met with a fair degree of success. Picon and Desbois (1990) statistically related Meteosat monthly mean water vapor radiances to six standard pressure levels of the European Centre for Medium Range Weather Forecast (ECMWF) model vertical velocities and found correlation coefficients of about 0.50 or less. This paper presents some preliminary results of viewing climatological satellite water vapor data in a different fashion. Specifically, we attempt to infer the three dimensional flow characteristics of the mid- to upper troposphere as portrayed by GOES VAS during the warm ENSO event (1987) and a subsequent cold period in 1998.

Lerner, Jeffrey A.↗
NASA NTRS1989

Stratospheric effects of solar ultraviolet variations on the solar rotation time scale

A summary is presented of some current work on measurement and interpretation of stratospheric ozone and temperature responses to observed short term solar ultraviolet variations. Although some studies have yielded provisional evidence for a nearly in-phase ozone-solar cycle relationship, they extend at most over only one or two 11 year cycles so the statistical significance of the correlations is not large. Similarly, the relatively short lengths of individual satellite data sets combined with the problem of estimating the effect of changes in instrument sensitivity (drift) during the observing period have complicated attempts to infer long term or solar cycle ozone trends. The solar rotation and active region development time scale provides an alternate time scale for which detailed studies of middle atmospheric ozone and temperature responses to solar ultraviolet variability are currently possible using available satellite data sets. At tropical latitudes where planetary wave amplitudes are relatively small, clear correlative evidence for the existence of middle atmospheric ozone and temperature responses to short term solar ultraviolet variations has been obtained in recent years. These measurements will ultimately allow improved empirical and theoretical calculations of longer term solar induced ozone and temperature variations at low and middle latitudes.

Hood, L. L.↗