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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 397 records · Page 22

All sources NASA NTRS DOE OSTI

DOE OSTI2022

MIDAS: Modeling Individual Differences using Advanced Statistics

This research explores novel methods for extracting relevant information from EEG data to characterize individual differences in cognitive processing. Our approach combines expertise in machine learning, statistics, and cognitive science, advancing the state-of-the art in all three domains. Specifically, by using cognitive science expertise to interpret results and inform algorithm development, we have developed a generalizable and interpretable machine learning method that can accurately predict individual differences in cognition. The output of the machine learning method revealed surprising features of the EEG data that, when interpreted by the cognitive science experts, provided novel insights to the underlying cognitive task. Additionally, the outputs of the statistical methods show promise as a principled approach to quickly find regions within the EEG data where individual differences lie, thereby supporting cognitive science analysis and informing machine learning models. This work lays methodological ground work for applying the large body of cognitive science literature on individual differences to high consequence mission applications.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Advances in statistical methods for cancer surveillance research: an age-period-cohort perspective

Background: Analysis of Lexis diagrams (population-based cancer incidence and mortality rates indexed by age group and calendar period) requires specialized statistical methods. However, existing methods have limitations that can now be overcome using new approaches. Methods: We assembled a “toolbox” of novel methods to identify trends and patterns by age group, calendar period, and birth cohort. We evaluated operating characteristics across 152 cancer incidence Lexis diagrams compiled from United States (US) Surveillance, Epidemiology and End Results Program data for 21 leading cancers in men and women in four race and ethnicity groups (the “cancer incidence panel”). Results: Nonparametric singular values adaptive kernel filtration (SIFT) decreased the estimated root mean squared error by 90% across the cancer incidence panel. A novel method for semi-parametric age-period-cohort analysis (SAGE) provided optimally smoothed estimates of age-period-cohort (APC) estimable functions and stabilized estimates of lack-of-fit (LOF). SAGE identified statistically significant birth cohort effects across the entire cancer panel; LOF had little impact. As illustrated for colon cancer, newly developed methods for comparative age-period-cohort analysis can elucidate cancer heterogeneity that would otherwise be difficult or impossible to discern using standard methods. Conclusions: Cancer surveillance researchers can now identify fine-scale temporal signals with unprecedented accuracy and elucidate cancer heterogeneity with unprecedented specificity. Birth cohort effects are ubiquitous modulators of cancer incidence in the US. The novel methods described here can advance cancer surveillance research.

60 APPLIED LIFE SCIENCES↗

NASA NTRS2002

A statistical model for interpreting computerized dynamic posturography data

Computerized dynamic posturography (CDP) is widely used for assessment of altered balance control. CDP trials are quantified using the equilibrium score (ES), which ranges from zero to 100, as a decreasing function of peak sway angle. The problem of how best to model and analyze ESs from a controlled study is considered. The ES often exhibits a skewed distribution in repeated trials, which can lead to incorrect inference when applying standard regression or analysis of variance models. Furthermore, CDP trials are terminated when a patient loses balance. In these situations, the ES is not observable, but is assigned the lowest possible score--zero. As a result, the response variable has a mixed discrete-continuous distribution, further compromising inference obtained by standard statistical methods. Here, we develop alternative methodology for analyzing ESs under a stochastic model extending the ES to a continuous latent random variable that always exists, but is unobserved in the event of a fall. Loss of balance occurs conditionally, with probability depending on the realized latent ES. After fitting the model by a form of quasi-maximum-likelihood, one may perform statistical inference to assess the effects of explanatory variables. An example is provided, using data from the NIH/NIA Baltimore Longitudinal Study on Aging.

NASA Discipline Neuroscience↗

NASA NTRS1995

Statistical and linguistic features of DNA sequences

We present evidence supporting the idea that the DNA sequence in genes containing noncoding regions is correlated, and that the correlation is remarkably long range--indeed, base pairs thousands of base pairs distant are correlated. We do not find such a long-range correlation in the coding regions of the gene. We resolve the problem of the "non-stationary" feature of the sequence of base pairs by applying a new algorithm called Detrended Fluctuation Analysis (DFA). We address the claim of Voss that there is no difference in the statistical properties of coding and noncoding regions of DNA by systematically applying the DFA algorithm, as well as standard FFT analysis, to all eukaryotic DNA sequences (33 301 coding and 29 453 noncoding) in the entire GenBank database. We describe a simple model to account for the presence of long-range power-law correlations which is based upon a generalization of the classic Levy walk. Finally, we describe briefly some recent work showing that the noncoding sequences have certain statistical features in common with natural languages. Specifically, we adapt to DNA the Zipf approach to analyzing linguistic texts, and the Shannon approach to quantifying the "redundancy" of a linguistic text in terms of a measurable entropy function. We suggest that noncoding regions in plants and invertebrates may display a smaller entropy and larger redundancy than coding regions, further supporting the possibility that noncoding regions of DNA may carry biological information.

Non-NASA Center↗

NASA NTRS2000

A statistical model of the human core-temperature circadian rhythm

We formulate a statistical model of the human core-temperature circadian rhythm in which the circadian signal is modeled as a van der Pol oscillator, the thermoregulatory response is represented as a first-order autoregressive process, and the evoked effect of activity is modeled with a function specific for each circadian protocol. The new model directly links differential equation-based simulation models and harmonic regression analysis methods and permits statistical analysis of both static and dynamical properties of the circadian pacemaker from experimental data. We estimate the model parameters by using numerically efficient maximum likelihood algorithms and analyze human core-temperature data from forced desynchrony, free-run, and constant-routine protocols. By representing explicitly the dynamical effects of ambient light input to the human circadian pacemaker, the new model can estimate with high precision the correct intrinsic period of this oscillator ( approximately 24 h) from both free-run and forced desynchrony studies. Although the van der Pol model approximates well the dynamical features of the circadian pacemaker, the optimal dynamical model of the human biological clock may have a harmonic structure different from that of the van der Pol oscillator.

NASA Discipline Regulatory Physiology↗

NASA NTRS2013

Statistical Analysis of CFD Solutions From the Fifth AIAA Drag Prediction Workshop

A graphical framework is used for statistical analysis of the results from an extensive N-version test of a collection of Reynolds-averaged Navier-Stokes computational fluid dynamics codes. The solutions were obtained by code developers and users from North America, Europe, Asia, and South America using a common grid sequence and multiple turbulence models for the June 2012 fifth Drag Prediction Workshop sponsored by the AIAA Applied Aerodynamics Technical Committee. The aerodynamic configuration for this workshop was the Common Research Model subsonic transport wing-body previously used for the 4th Drag Prediction Workshop. This work continues the statistical analysis begun in the earlier workshops and compares the results from the grid convergence study of the most recent workshop with previous workshops.

Statistical analysis↗

NASA NTRS2013

Ensemble Statistical Post-Processing of the National Air Quality Forecast Capability: Enhancing Ozone Forecasts in Baltimore, Maryland

An ensemble statistical post-processor (ESP) is developed for the National Air Quality Forecast Capability (NAQFC) to address the unique challenges of forecasting surface ozone in Baltimore, MD. Air quality and meteorological data were collected from the eight monitors that constitute the Baltimore forecast region. These data were used to build the ESP using a moving-block bootstrap, regression tree models, and extreme-value theory. The ESP was evaluated using a 10-fold cross-validation to avoid evaluation with the same data used in the development process. Results indicate that the ESP is conditionally biased, likely due to slight overfitting while training the regression tree models. When viewed from the perspective of a decision-maker, the ESP provides a wealth of additional information previously not available through the NAQFC alone. The user is provided the freedom to tailor the forecast to the decision at hand by using decision-specific probability thresholds that define a forecast for an ozone exceedance. Taking advantage of the ESP, the user not only receives an increase in value over the NAQFC, but also receives value for An ensemble statistical post-processor (ESP) is developed for the National Air Quality Forecast Capability (NAQFC) to address the unique challenges of forecasting surface ozone in Baltimore, MD. Air quality and meteorological data were collected from the eight monitors that constitute the Baltimore forecast region. These data were used to build the ESP using a moving-block bootstrap, regression tree models, and extreme-value theory. The ESP was evaluated using a 10-fold cross-validation to avoid evaluation with the same data used in the development process. Results indicate that the ESP is conditionally biased, likely due to slight overfitting while training the regression tree models. When viewed from the perspective of a decision-maker, the ESP provides a wealth of additional information previously not available through the NAQFC alone. The user is provided the freedom to tailor the forecast to the decision at hand by using decision-specific probability thresholds that define a forecast for an ozone exceedance. Taking advantage of the ESP, the user not only receives an increase in value over the NAQFC, but also receives value for

ozone↗

NASA NTRS2016

Statistical Analysis of Instantaneous Frequency Scaling Factor as Derived From Optical Disdrometer Measurements At KQ Bands

The rain rate data and statistics of a location are often used in conjunction with models to predict rain attenuation. However, the true attenuation is a function not only of rain rate, but also of the drop size distribution (DSD). Generally, models utilize an average drop size distribution (Laws and Parsons or Marshall and Palmer [1]). However, individual rain events may deviate from these models significantly if their DSD is not well approximated by the average. Therefore, characterizing the relationship between the DSD and attenuation is valuable in improving modeled predictions of rain attenuation statistics. The DSD may also be used to derive the instantaneous frequency scaling factor and thus validate frequency scaling models. Since June of 2014, NASA Glenn Research Center (GRC) and the Politecnico di Milano (POLIMI) have jointly conducted a propagation study in Milan, Italy utilizing the 20 and 40 GHz beacon signals of the Alphasat TDP#5 Aldo Paraboni payload. The Ka- and Q-band beacon receivers provide a direct measurement of the signal attenuation while concurrent weather instrumentation provides measurements of the atmospheric conditions at the receiver. Among these instruments is a Thies Clima Laser Precipitation Monitor (optical disdrometer) which yields droplet size distributions (DSD); this DSD information can be used to derive a scaling factor that scales the measured 20 GHz data to expected 40 GHz attenuation. Given the capability to both predict and directly observe 40 GHz attenuation, this site is uniquely situated to assess and characterize such predictions. Previous work using this data has examined the relationship between the measured drop-size distribution and the measured attenuation of the link [2]. The focus of this paper now turns to a deeper analysis of the scaling factor, including the prediction error as a function of attenuation level, correlation between the scaling factor and the rain rate, and the temporal variability of the drop size distribution both within a given rain event and across different varieties of rain events. Index Terms-drop size distribution, frequency scaling, propagation losses, radiowave propagation.

Radio Attenuation↗

NASA NTRS2016

Statistical Analysis of Instantaneous Frequency Scaling Factor as Derived From Optical Disdrometer Measurements At KQ Bands

The rain rate data and statistics of a location are often used in conjunction with models to predict rain attenuation. However, the true attenuation is a function not only of rain rate, but also of the drop size distribution (DSD). Generally, models utilize an average drop size distribution (Laws and Parsons or Marshall and Palmer. However, individual rain events may deviate from these models significantly if their DSD is not well approximated by the average. Therefore, characterizing the relationship between the DSD and attenuation is valuable in improving modeled predictions of rain attenuation statistics. The DSD may also be used to derive the instantaneous frequency scaling factor and thus validate frequency scaling models. Since June of 2014, NASA Glenn Research Center (GRC) and the Politecnico di Milano (POLIMI) have jointly conducted a propagation study in Milan, Italy utilizing the 20 and 40 GHz beacon signals of the Alphasat TDP#5 Aldo Paraboni payload. The Ka- and Q-band beacon receivers provide a direct measurement of the signal attenuation while concurrent weather instrumentation provides measurements of the atmospheric conditions at the receiver. Among these instruments is a Thies Clima Laser Precipitation Monitor (optical disdrometer) which yields droplet size distributions (DSD); this DSD information can be used to derive a scaling factor that scales the measured 20 GHz data to expected 40 GHz attenuation. Given the capability to both predict and directly observe 40 GHz attenuation, this site is uniquely situated to assess and characterize such predictions. Previous work using this data has examined the relationship between the measured drop-size distribution and the measured attenuation of the link]. The focus of this paper now turns to a deeper analysis of the scaling factor, including the prediction error as a function of attenuation level, correlation between the scaling factor and the rain rate, and the temporal variability of the drop size distribution both within a given rain event and across different varieties of rain events. Index Terms-drop size distribution, frequency scaling, propagation losses, radiowave propagation.

Statistics↗

NASA NTRS2017

Statistical Analysis of CFD Solutions from the 6th AIAA CFD Drag Prediction Workshop

A graphical framework is used for statistical analysis of the results from an extensive N-version test of a collection of Reynolds-averaged Navier-Stokes computational fluid dynamics codes. The solutions were obtained by code developers and users from North America, Europe, Asia, and South America using both common and custom grid sequences as well as multiple turbulence models for the June 2016 6th AIAA CFD Drag Prediction Workshop sponsored by the AIAA Applied Aerodynamics Technical Committee. The aerodynamic configuration for this workshop was the Common Research Model subsonic transport wing-body previously used for both the 4th and 5th Drag Prediction Workshops. This work continues the statistical analysis begun in the earlier workshops and compares the results from the grid convergence study of the most recent workshop with previous workshops.

Statistical analysis↗

NASA NTRS2017

Relevance of the American Statistical Society's Warning on P-Values for Conjunction Assessment

On March 7, 2016, the American Statistical Association (ASA) issued an online editorial paper on the "context, process, and purpose of p-values." According to the paper, "the statement articulates in non-technical terms a few select principles that could improve the conductor interpretation of quantitative science, according to widespread consensus in the statistical community." This "consensus" statement was accompanied by 21 "commentaries" expressing a diversity of opinions among the panel of experts ASA convened. In the present work, we express our view that the ASA p-value warning has relevance to the space object Conjunction Assessment (CA) community.

p-values↗

NASA NTRSDate not supplied

Statistical Descriptors of Composite Fiber Aggregation

This study introduces a method of characterizing fiber aggregation and resin rich regions in composite microstructures. Microscale models of representative elements (RVE) need to be indicative of the extend of clustering (i.e. close fiber-to-fiber interaction) and resin rich “pools” which may impact the overall strength and performance of a composite structure. This algorithm was used to evaluate different unidirectional 2-D microstructure scans, which will be compared to their manufacturing method or any special treatment processes. These cluster and pool scan statistics can be used as criteria to judge statistical equivalency of artificially constructed microstructures.

Statistics↗

NASA NTRSDate not supplied

Artificial Generation of 2-D Fiber Reinforced Composite Microstructures with Statistically Equivalent Features

Fiber reinforced composites are used widely for their high strength and low weight advantages in various aerospace and automotive applications. While their use may be sought after, modeling of these material requires increasing fidelity at the lower scales to capture accurate material behavior under loading. The first steps in creating statistically equivalent models to real life cases is developing a method of rapid evaluation and artificial microstructure generation. The outlined work is capable of tracking microscale fiber positions and determining regions of localized volume fraction extrema (high and low end). Groupings of high and low volume fraction regions are called clusters and their geometry is used to characterize the microstructure. These cluster features can be evaluated for both artificial models and actual scans, allowing correlation to be established which can ultimately be used to regenerate statistically equivalent models. The results of this work show that if one feature is to be correlated, a model can be generated which matches almost exactly. But once more features are equally taken into account, the regeneration loses accuracy.

micromechanics↗

DOE OSTI2020

Collaborative Exploration of Scientific Datasets Using Immersive and Statistical Visualization: Preprint

We discuss the value of collaborative, immersive visualization for the exploration of scientific datasets and review techniques and tools that have been developed and deployed at the National Renewable Energy Laboratory (NREL). We believe that collaborative visualizations linking statistical interfaces and graphics on laptops and high-performance computing (HPC) with 3D visualizations on immersive displays (head-mounted displays and large-scale immersive environments) enable scientific workflows that further rapid exploration of large, high-dimensional datasets by teams of analysts. We present a framework, PlottyVR, that blends statistical tools, general-purpose programming environments, and simulation with 3D visualizations. To contextualize this framework, we propose a categorization and loose taxonomy of collaborative visualization and analysis techniques. Finally, we describe how scientists and engineers have adopted this framework to investigate large, complex datasets.

collaborative visualization↗

DOE OSTI2021

Collaborative Exploration of Scientific Datasets Using Immersive and Statistical Visualization

collaborative visualization↗

DOE OSTI2023

Wormholes from heavy operator statistics in AdS/CFT

We construct higher dimensional Euclidean AdS wormhole solutions that reproduce the statistical description of the correlation functions of an ensemble of heavy CFT operators. We consider an operator which effectively backreacts on the geometry in the form of a thin shell of dust particles. Assuming dynamical chaos in the form of the ETH ansatz, we demonstrate that the semiclassical path integral provides an effective statistical description of the microscopic features of the thin shell operator in the CFT. The Euclidean wormhole solutions provide microcanonical saddlepoint contributions to the cumulants of the correlation functions over the ensemble of operators. We finally elaborate on the role of these wormholes in the context of non-perturbative violations of bulk global symmetries in AdS/CFT.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Evaluation of precipitation indices in suites of dynamically and statistically downscaled regional climate models over Florida

Abstract The present work evaluates historical precipitation and its indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) in suites of dynamically and statistically downscaled regional climate models (RCMs) against NOAA’s Global Historical Climatology Network Daily (GHCN-Daily) dataset over Florida. The models examined here are: (1) nested RCMs involved in the North American CORDEX (NA-CORDEX) program, (2) variable resolution Community Earth System Models (VR-CESM), (3) Coupled Model Intercomparison Project phase 5 (CMIP5) models statistically downscaled using localized constructed analogs (LOCA) technique. To quantify observational uncertainty, three in situ-based (PRISM, Livneh, CPC) and three reanalysis (ERA5, MERRA2, NARR) datasets are also evaluated against the station data. The reanalyses and dynamically downscaled RCMs generally underestimate the magnitude of the monthly precipitation and the frequency of the extreme rainfall in summer. The models forced with CanESM2 miss the phase of the seasonality of extreme precipitation. All models and reanalyses severely underestimate both the mean and interannual variability of mean wet-day precipitation (SDII), consecutive dry days (CDD), and overestimate consecutive wet days (CWD). Metric analysis suggests large uncertainty across NA-CORDEX models. Both the LOCA and VR-CESM models perform better than the majority of models. Overall, RegCM4 and WRF models perform poorer than the median model performance. The performance uncertainty across models is comparable to that in the reanalyses. Specifically, NARR performs poorer than the median model performance in simulating the mean indices and MERRA2 performs worse than the majority of models in capturing the interannual variability of the indices.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Enhancing Interpretability in Generative Modeling: Statistically Disentangled Latent Spaces Guided by Generative Factors in Scientific Datasets

This study addresses the challenge of statistically extracting generative factors from complex, high-dimensional datasets in unsupervised or semi-supervised settings. We investigate encoder-decoder-based generative models for nonlinear dimensionality reduction, focusing on disentangling low-dimensional latent variables corresponding to independent physical factors. Introducing Aux-VAE, a novel architecture within the classical Variational Autoencoder framework, we achieve disentanglement with minimal modifications to the standard VAE loss function by leveraging prior statistical knowledge through auxiliary variables. These variables guide the shaping of the latent space by aligning latent factors with learned auxiliary variables. We validate the efficacy of Aux-VAE through comparative assessments on multiple datasets, including astronomical simulations.

97 MATHEMATICS AND COMPUTING↗