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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

BPS fivebrane stars. Part I. Expectation values of observables

We study ensembles of 1/2-BPS bound states of fundamental strings and NS-fivebranes (NS5-F1 states) in the AdS decoupling limit. We revisit a solution corresponding to an ensemble average of these bound states, and find that the appropriate duality frame for describing the near-source structure is the T-dual NS5-P frame, where the bound state is a collection of momentum waves on the fivebranes. We find that the fivebranes are generically well-separated; this property results in the applicability of perturbative string theory. The geometry sourced by the typical microstate is not close to that of an extremal non-rotating black hole; instead the fivebranes occupy a ball whose radius is parametrically much larger than the “stretched horizon” scale of the corresponding black hole. These microstates are thus better characterized as BPS fivebrane stars than as small black holes.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2001

Free-form Airfoil Shape Optimization Under Uncertainty Using Maximum Expected Value and Second-order Second-moment Strategies

Free-form shape optimization of airfoils poses unexpected difficulties. Practical experience has indicated that a deterministic optimization for discrete operating conditions can result in dramatically inferior performance when the actual operating conditions are different from the - somewhat arbitrary - design values used for the optimization. Extensions to multi-point optimization have proven unable to adequately remedy this problem of "localized optimization" near the sampled operating conditions. This paper presents an intrinsically statistical approach and demonstrates how the shortcomings of multi-point optimization with respect to "localized optimization" can be overcome. The practical examples also reveal how the relative likelihood of each of the operating conditions is automatically taken into consideration during the optimization process. This is a key advantage over the use of multipoint methods.

Huyse, Luc↗
NASA NTRSDate not supplied

The GeoGLOWS Project: Essential Water Variables and Observations – Expected Value Chain, Products, Examples

Essential Water Variables (EWV) were developed over the last decade to ensure that key datasets were made available for closing the global water budget, and addressing the many international actions that require water data. Satellite, surface, and numerical model data all have a role in providing EWVs, and it is a key point that these data must be merged and then fed into decision support applications to answer all the needs of the panoply of user communities. The more specialized the applications become, the higher the demand for development and maintenance resources. Some examples of applications include the NASA Goddard Giovanni, which provides on-line data access and simple analysis; the Global Flood Monitor, which focuses on precipitation-driven flooding; the NASA Landslide Hazard Assessment for Situational Awareness, which similarly treats landslides; and the Global Streamflow Forecasting Project, which provides multiple decision support interfaces, as well as a general toolkit for building new interfaces.

Essential Water Variables↗
NASA NTRS1995

The anisotropy in the xosmic microwave background at degree angular scales

We detect anisotropy in the cosmic microwave background (CMB) at degree angular scales and confirm a previous detection reported by Wollack et al. (1993). The root-mean-squared amplitude of the fluctuations is 44(sup+13)(sub -7)microK. This may be expressed as the square root of the angular power spectrum in a band of multiples between l(sub eff)=69(sup +29)(sub -22). We find delta T(sub l) = square root of (l(2l+1) expected value of ((absolute value of (a(sup m)(sub L)))(exp 2))/(4xpi))= 42(sup +12)(sub -7). The measured spectral index of the fluctuations is consistent with zero, the value expected for the CMB. The spectral index corresponding to Galactic free-free emission, the most likely foreground contaminant, is rejected at approximately 3 sigma. The analysis is based on three independent data sets. The first, taken in 1993, spans the 26-36 GHz frequency range with three frequency bands; the second was taken with the same radiometer as the first but during an independent observing campaign in 1994; and the third, also taken in 1994, spans the 36-46 GHz range in three bands. For each telescope position and radiometer channel, the drifts in the instrument offset are less than or equal to 4 microK/day averaged over a period of 1 month. The dependence of the inferred anisotropy on the calibration and data editing is addressed.

Netterfield, C. B.↗
NASA NTRS2004

Calibration Software for Use with Jurassicprok

The Jurassicprok Interferometric Calibration Software (also called "Calibration Processor" or simply "CP") estimates the calibration parameters of an airborne synthetic-aperture-radar (SAR) system, the raw measurement data of which are processed by the Jurassicprok software described in the preceding article. Calibration parameters estimated by CP include time delays, baseline offsets, phase screens, and radiometric offsets. CP examines raw radar-pulse data, single-look complex image data, and digital elevation map data. For each type of data, CP compares the actual values with values expected on the basis of ground-truth data. CP then converts the differences between the actual and expected values into updates for the calibration parameters in an interferometric calibration file (ICF) and a radiometric calibration file (RCF) for the particular SAR system. The updated ICF and RCF are used as inputs to both Jurassicprok and to the companion Motion Measurement Processor software (described in the following article) for use in generating calibrated digital elevation maps.

Chapin, Elaine↗
NASA NTRS2019

Multilevel Monte Carlo Estimation of Unbiased Expectation via Sample Reuse and the Low Variance Estimation of Asymptotic Rates

A new variant of the multilevel Monte Carlo estimator [5, 3, 9, 12] is presented for the estimation of expectation statistics that utilizes sample reuse in specified levels, explicitly removes approximation error bias associated with numerically computed output quantities of interest that have an asymptotic limit behavior, and permits a low variance estimate of the asymptotic rate of convergence to that limit. In addition, it is shown that this new multilevel Monte Carlo variant can yield a computational cost savings. A review of Monte Carlo and multilevel Monte Carlo estimators is presented that includes analysis of expected value, expected mean squared error, and the calculation of optimized multilevel sample size parameters. The multilevel Monte Carlo estimator produces estimates of expectation for numerically approximated output quantities of interest that are biased by approximation error. When the quantity of interest can be modeled as the asymptotic limit of numerically approximated output quantities of interest, it is theoretically possible to remove this approximation error bias in the multilevel Monte Carlo estimator. In actual implementations, however, this procedure is unreliable due to statistical variability and inaccuracy in estimating the needed asymptotic limit. Analysis and numerical experiment show that the proposed variant of the multilevel Monte Carlo method greatly reduces (in some cases eliminates) the statistical variability in this limit estimation.

Barth, Timothy↗
NASA NTRS1994

Accurate Evaluation of Quantum Integrals

Combining an appropriate finite difference method with Richardson's extrapolation results in a simple, highly accurate numerical method for solving a Schr\"{o}dinger's equation. Important results are that error estimates are provided, and that one can extrapolate expectation values rather than the wavefunctions to obtain highly accurate expectation values. We discuss the eigenvalues, the error growth in repeated Richardson's extrapolation, and show that the expectation values calculated on a crude mesh can be extrapolated to obtain expectation values of high accuracy.

Galant, David C.↗
NASA NTRS1995

Accurate Evaluation of Quantum Integrals

Combining an appropriate finite difference method with Richardson's extrapolation results in a simple, highly accurate numerical method for solving a Schrodinger's equation. Important results are that error estimates are provided, and that one can extrapolate expectation values rather than the wavefunctions to obtain highly accurate expectation values. We discuss the eigenvalues, the error growth in repeated Richardson's extrapolation, and show that the expectation values calculated on a crude mesh can be extrapolated to obtain expectation values of high accuracy.

Galant, D. C.↗
DOE OSTI2020

Effective Missing Value Imputation Methods for Building Monitoring Data

To understand behaviors of natural and man-made events, such as energy consumption of buildings, which accounts for 40% of energy uses in the US, we deploy automated monitoring devices to record periodic observations. However, such experimental and observation data often contains problems and irregularities that have to be cleaned up before analyses. Due to various conditions affecting sensor operations, the communication channels, recording steps, or the recording media, the recorded data might have missing values, errors, or anomalous values. An effective way to clean up these problems is to replace these missing values, errors and anomalous values with expected values, a process generally known as imputation. In this work, we survey commonly used missing value imputation techniques and compare their performance on a set of building monitoring data. To compare the different types of sensor measurements with widely varying characteristics, we use normalized root mean squared error (NRMSE) as the key metric for the effectiveness of the imputation methods. We additionally consider periodicity and run time when considering comparing methods. Through extensive testing, we find that for small gap sizes, up to 8 consecutive missing values, linear interpolation performs the best; for larger gaps stretching up to 48 consecutive missing values, K-nearest neighbors provides the most accurate imputations; for even larger gaps, more computational intensive methods, such as matrix factorization, achieve the smallest NRMSE. Additionally, we observe that these computationally intensive algorithms not only provide accurate imputations for large gaps, but are also more robust across all types of sensors.

Cho, B↗
NASA NTRS1974

The Hubble relation for nonstandard candles and the origin of the redshift of quasars

It is shown that the magnitude-log (redshift) relation for brightest quasars can have a slope different from the value expected for standard candles. The value of this slope depends on the luminosity function and its evolution. Therefore the difference of this slope from the expected value cannot be used as evidence against the cosmological origin of the redshift of the quasars. It is shown that the observed variation of the luminosity of the brightest objects with redshift is consistent with the cosmological hypothesis and that it agrees with (and perhaps could be used to complement) the luminosity function obtained from V/Vm analysis. It is also shown that the nonzero slope of the magnitude-log (redshift) relation rules out the local quasar hypothesis, where it is assumed that the sources are nearby (less than 500 Mpc), that the bulk of their redshift is intrinsic, and that there is no dependence on distance of the intrinsic properties of the sources.

Petrosian, V.↗
NASA NTRS1992

Engine monitoring display study

The current study is part of a larger NASA effort to develop displays for an engine-monitoring system to enable the crew to monitor engine parameter trends more effectively. The objective was to evaluate the operational utility of adding three types of information to the basic Boeing Engine Indicating and Crew Alerting System (EICAS) display formats: alphanumeric alerting messages for engine parameters whose values exceed caution or warning limits; alphanumeric messages to monitor engine parameters that deviate from expected values; and a graphic depiction of the range of expected values for current conditions. Ten training and line pilots each flew 15 simulated flight scenarios with five variants of the basic EICAS format; these variants included different combinations of the added information. The pilots detected engine problems more quickly when engine alerting messages were included in the display; adding a graphic depiction of the range of expected values did not affect detection speed. The pilots rated both types of alphanumeric messages (alert and monitor parameter) as more useful and easier to interpret than the graphic depiction. Integrating engine parameter messages into the EICAS alerting system appears to be both useful and preferred.

Hornsby, Mary E.↗
NASA NTRS1989

Probabilities and statistics for backscatter estimates obtained by a scatterometer with applications to new scatterometer design data

The values of the Normalized Radar Backscattering Cross Section (NRCS), sigma (o), obtained by a scatterometer are random variables whose variance is a known function of the expected value. The probability density function can be obtained from the normal distribution. Models for the expected value obtain it as a function of the properties of the waves on the ocean and the winds that generated the waves. Point estimates of the expected value were found from various statistics given the parameters that define the probability density function for each value. Random intervals were derived with a preassigned probability of containing that value. A statistical test to determine whether or not successive values of sigma (o) are truly independent was derived. The maximum likelihood estimates for wind speed and direction were found, given a model for backscatter as a function of the properties of the waves on the ocean. These estimates are biased as a result of the terms in the equation that involve natural logarithms, and calculations of the point estimates of the maximum likelihood values are used to show that the contributions of the logarithmic terms are negligible and that the terms can be omitted.

Pierson, Willard J., Jr.↗
NASA NTRS1972

Parameter uncertainties in control system design.

Development of a design method for including the effects of parameter uncertainties in the design of linear control systems. The approach taken to this problem may be classified as a special case of the stochastic control problem. Thus the formulation is based on the minimization of the expected value of a quadratic performance index defined in terms of the system state vector. The uncertainty in the value of the performance index is the result of the statistical nature of the system parameters rather than a random input signal. It is shown that the expected value of the performance index may be written as a sum of two terms under the assumption of first-order variations of the system state. The first of these terms expresses the nominal performance of the system when the system parameters assume their mean values. The second term represents the effect of the uncertainties on the expected value of the performance index, and is interpreted as an index of system sensitivity.

Palsson, T.↗
DOE OSTI2022

Bayesian Optimization Framework for Imperfect Data or Models

Conventional Bayesian optimization methods implicitly assume that the data and model being optimized are “perfect.” This assumption leads to inaccurate posterior probability distribution functions (PDFs) when applied to “imperfect” data or models. The new Bayesian optimization framework presented in this report provides a way to parameterize the effect of imperfections usually encountered in a prior PDF of generalized data or a model on the posterior PDF. The effects of imperfections are parameterized by a set of constraints imposed on the posterior expectation values of deviations between the data and the model and on their covariance matrix elements. A particular set of values for these constraints conveys an evaluator’s best estimate of the effect of imperfections on the corresponding posterior expectation values. When a prior PDF of generalized data is assumed to be normal, an expression for a posterior PDF satisfying an arbitrary set of constraints is derived analytically for linear models. An analogous iterative algorithm is given for nonlinear models. The corresponding posterior PDF should be used to estimate any posterior expectation values in the presence of imperfections parameterized by that set of constraints. A posterior PDF of a conventional Bayesian optimization method is recovered analytically when all evaluator-specified constraints are set to zero (i.e., in the absence of any imperfections). The analytical expressions derived in this report for normal PDFs and linear models were verified numerically by a Metropolis–Hastings Monte Carlo method. The methods presented herein could be applied to any kind of data or models, including differential cross-section data or integral benchmark experiments.

97 MATHEMATICS AND COMPUTING↗