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At least 1,315 records · Page 73
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Count Every Trip: Finding the Uncertainty in Energy Estimates Made from Inferred Travel Modes

To properly inform transport policy and infrastructure changes, transportation related metrics need both measured values and uncertainties of those values. Travel monitoring smartphone apps can record people's travel behavior, but trip data quality is limited by sensor errors, user labeling rates and the accuracy of inference algorithms used for travel diary creation. We discuss the use of phone app recorded travel diary data to estimate energy consumption, and propose the use of propagation of variance to find error bars for such estimates. We define energy consumption for one trip as trip length times the energy intensity per distance unit of the travel mode used. We characterize trip length errors with relative error and inferred trip mode errors with confusion matrix columns. The resulting variances of each measurement are then propagated to the final calculated energy consumption. We tested our uncertainty methods on a dataset that used phone app data combined with prompted recall, consisting of 92,234 labeled trips for over 500,000 miles. Accounting for uncertainty using expected energy intensities and variance propagation gives a dataset-wide aggregate energy consumption percent error of about 9%, within one standard deviation from the truth. Future work could involve applying similar methods to other travel diary based metrics.

ADVANCED PROPULSION SYSTEMS↗
NASA NTRSDate not supplied

Onboard Hyperspectral Image Classification via Transfer Learning for Communication-Limited Spacecraft

Employing deep-learning and artificial-intelligence (AI) techniques onboard spacecraft can dramatically improve priority data selection to ensure more effective use of the available downlink. However, deployment of effective deep-learning models requires significant training on the ground, which may not be feasible, due to limited data available in an unexplored environment. Therefore, this research explores building robust classification models for onboard data processing where training data is highly limited using transfer-learning techniques. In this paper, we focus on the use case of hyperspectral imaging for remote sensing, a domain where the high dimensionality of the data from the sensor can rapidly saturate the downlink bandwidth. With this bottleneck, there is an impending need to autonomously and robustly classify data onboard to optimize downlink of high-impact measurements, thus maximizing the scientific utility per bit transmitted to the ground. This paper examines the use of deep neural networks onboard for hyperspectral image classification in a communication-limited scenario to analyze how the models perform with limited training data. The use of transfer learning can ameliorate the issue of poor generalization by transferring features learned from training on a large source dataset for one classification task to the target classification task with limited training data. For two deep-learning models from literature, we compare the accuracy of the models trained using transfer learning to models trained from scratch using a random weight initialization with varying amounts of training data. We demonstrate the feasibility and performance of running inference of the deep-learning models on representative flight-like hardware.

Advanced Avionics, Machine Learning, Data Processi↗
NASA NTRS1983

Computation of Jupiter interior models from gravitational inversion theory

Spacecraft measurements of Jupiter have provided the mass, standard pressure level radius, rotation law, internal mass distribution multipole moments, and internal composition and temperature distribution constraints, for the present implementation of a method for deriving planetary interior models that exactly satisfy a set of N gravitational constraints by means of appropriate iteration. The models are not forced to fit the more indirectly derived constraints, which are instead used as conistency checks. In the case of an He mass fraction in the envelope Y of 0.2, the inferred pressure at a mass density of about 0.2 g/cu cm is about a factor of 2 higher than would be indicated by experimental H compression data in the relevant pressure range of 100,000 to one million bar. The inferred pressure distribution is in better agreement with the shock data for a nominal Y value of 0.3 + or - 0.05.

Hubbard, W. B.↗
DOE OSTI2026

Discovery of Probabilistic Dirichlet-to-Neumann Maps on Graphs

Dirichlet-to-Neumann maps enable the coupling of multiphysics simulations across computational subdomains by ensuring continuity of state variables and fluxes at artificial interfaces. We present a novel method for learning Dirichlet-to-Neumann maps on graphs using Gaussian processes, specifically for problems where the data obey a conservation law arising from an underlying partial differential equation. Our approach combines discrete exterior calculus and nonlinear optimal recovery to infer relationships between vertex and edge values. This framework yields data-driven predictions with uncertainty quantification across the entire graph, even when observations are limited to a subset of vertices and edges. By minimizing the reproducing kernel Hilbert space norm while penalizing kernel complexity through maximum likelihood estimation, our method ensures that the resulting surrogate strictly enforces conservation laws without overfitting. We demonstrate our method on two representative applications: subsurface flow in fracture networks and arterial blood flow. Finally, the results demonstrate that the method maintains high accuracy and well-calibrated uncertainty estimates even under severe data scarcity, highlighting its potential for scientific applications where limited data and reliable uncertainty quantification are critical.

Dirichlet-to-Neumann map↗
NASA NTRS1992

Evaluation of fuzzy inference systems using fuzzy least squares

Efforts to develop evaluation methods for fuzzy inference systems which are not based on crisp, quantitative data or processes (i.e., where the phenomenon the system is built to describe or control is inherently fuzzy) are just beginning. This paper suggests that the method of fuzzy least squares can be used to perform such evaluations. Regressing the desired outputs onto the inferred outputs can provide both global and local measures of success. The global measures have some value in an absolute sense, but they are particularly useful when competing solutions (e.g., different numbers of rules, different fuzzy input partitions) are being compared. The local measure described here can be used to identify specific areas of poor fit where special measures (e.g., the use of emphatic or suppressive rules) can be applied. Several examples are discussed which illustrate the applicability of the method as an evaluation tool.

Barone, Joseph M.↗
NASA NTRS2024

Evaluation of Vertical Patterns in Chlorophyll-A Derived From A Data Assimilating Model of Satellite-Based Ocean Color

Satellite-based sensors of ocean color have become the primary tool to infer changes in surface chlorophyll, while BGC-Argo floats are now filling the information gap at depth. Here we use BGC-Argo data to assess depth-resolved information on chlorophyll-a derived from an ocean biogeochemical model constrained by the assimilation of surface ocean color remote sensing. The data-assimilating model replicates well the general seasonality and meridional gradients in surface and depth-resolved chlorophyll-a inferred from the float array in the Southern Ocean. On average, the model tends to overestimate float-based chlorophyll, particularly at times and locations of high productivity such as the beginning of the spring bloom, subtropical deep chlorophyll maxima, and non-iron limited regions of the Southern Ocean. The highest model RMSE in the upper 50 m with respect to the float array is of 0.6 mg Chl m −3 , which should allow the detection of seasonal changes in float-based biomass (varying between 0.01 and >1 mg Chl m −3 ) but might hinder the identification of subtle changes in chlorophyll at narrow local scales. Both model and float profiling data show good agreement with in situ data from station ALOHA, with model estimates showing a slight accuracy edge in inferring depth-resolved observations. Uncertainties in float bio-optical estimates impede their use as a reliable benchmark for validation, but the general qualitative agreement between model and float data provides confidence in the ability of model to replicate biogeochemical features below the surface, where data is not directly constrained by the assimilation of satellite ocean color.

Lionel A Quintero↗
DOE OSTI2025

Dark Energy Survey Year 3 results: $w$CDM cosmology from simulation-based inference with persistent homology on the sphere

We present cosmological constraints from Dark Energy Survey Year 3 (DES Y3) weak lensing data using persistent homology, a topological data analysis technique that tracks how features like clusters and voids evolve across density thresholds. For the first time, we apply spherical persistent homology to galaxy survey data through the algorithm TopoS2, which is optimized for curved-sky analyses and HEALPix compatibility. Employing a simulation-based inference framework with the Gower Street simulation suite, specifically designed to mimic DES Y3 data properties, we extract topological summary statistics from convergence maps across multiple smoothing scales and redshift bins. After neural network compression of these statistics, we estimate the likelihood function and validate our analysis against baryonic feedback effects, finding minimal biases (under $0.3σ$) in the $Ω_\mathrm{m}-S_8$ plane. Assuming the $w$CDM model, our combined Betti numbers and second moments analysis yields $S_8 = 0.821 \pm 0.018$ and $Ω_\mathrm{m} = 0.304\pm0.037$-constraints 70% tighter than those from cosmic shear two-point statistics in the same parameter plane. Our results demonstrate that topological methods provide a powerful and robust framework for extracting cosmological information, with our spherical methodology readily applicable to upcoming Stage IV wide-field galaxy surveys.

Prat, J. [Nordita; Royal Inst. Tech., Sodertalje; ↗
DOE OSTI2025

An implementation of neural simulation-based inference for parameter estimation in ATLAS

Neural simulation-based inference (NSBI) is a powerful class of machine-learning-based methods for statistical inference that naturally handles high-dimensional parameter estimation without the need to bin data into low-dimensional summary histograms. Such methods are promising for a range of measurements, including at the Large Hadron Collider, where no single observable may be optimal to scan over the entire theoretical phase space under consideration, or where binning data into histograms could result in a loss of sensitivity. This work develops a NSBI framework for statistical inference, using neural networks to estimate probability density ratios, which enables the application to a full-scale analysis. It incorporates a large number of systematic uncertainties, quantifies the uncertainty due to the finite number of events in training samples, develops a method to construct confidence intervals, and demonstrates a series of intermediate diagnostic checks that can be performed to validate the robustness of the method. As an example, the power and feasibility of the method are assessed on simulated data for a simplified version of an off-shell Higgs boson couplings measurement in the four-lepton final states. This approach represents an extension to the standard statistical methodology used by the experiments at the Large Hadron Collider, and can benefit many physics analyses.

frequentist statistics↗
DOE OSTI2026

Structure-aware Initialization via Numerical Continuation and Informed Priors

Scientific machine learning (SciML) often operates in ill-conditioned, weakly identifiable regimes due to limited data or indirect observations. In such settings, optimization and inference are highly sensitive to the starting point, making initialization--often under-reported--a consequential degree of freedom. Random initialization is not a neutral default as it induces an implicit prior over candidate solutions and can systematically bias the result, producing large run-to-run variability. Here, we formalize this view by treating initialization as a hidden confounder in SciML and develop a unifying theory for structure-aware initialization via numerical continuation, constructing warm starts from related problem instances. Across representative tasks, including physics-informed neural networks, maximum likelihood estimation, and variational inference, warm starts have been shown to consistently reduce optimization effort and improve reliability.

Data integrity↗
NASA NTRS1988

Geodetic measurement of deformation in California

The very long baseline interferometry (VLBI) measurements made in the western U.S. since 1979 as part of the NASA Crustal Dynamics Project provide discrete samples of the temporal and spatial deformation field. The interpretation of the VLBI-derived rates of deformation requires an examination of geologic information and more densely sampled ground-based geodetic data. In the first two of three related studies embodying this thesis triangulation and trilateration data measured on two regional networks are processed, one in the central Mojave Desert and one in the Coast Ranges east of the San Andreas fault. At the spatial scales spanned by these local geodetic networks, auxiliary geologic and geophysical data have been utilized to examine the relation between measured incremental strain and the accommodation of strain seen in local geological structures, strain release in earthquakes, and principal stress directions inferred from in situ measurements. In the third study, VLBI data from stations distributed across the Pacific - North American plate boundary zone in the western United States are processed. The VLBI data have been used to constrain the integrated rate of deformation across portions of the continental plate boundary in California and to provide a tectonic framework to interpret regional geodetic and geologic studies.

Sauber, Jeanne Marie↗
NASA NTRS2007

Comparisons of Instantaneous TRMM Ground Validation and Satellite Rain Rate Estimates at Different Spatial Scales

This study provides a comprehensive inter-comparison of instantaneous rain estimates from the two rain sensors aboard the TRMM satellite with ground data from thee designated Ground Validation Sites: Kwajalein Atoll, Melbourne, Florida and Houston, Texas. The satellite rain retrievals utilize rain observations collected by the TRMM microwave imager (TMI) and the Precipitation Radar (PR) aboard the TRMM satellite. Three standard instantaneous rain products are the generated from the rain information retrieved from the satellite using the TMI, PR and Combined (COM) rain algorithms. The validation data set used in this study was obtained from instantaneous rain rates inferred from ground radars at each GV site. The first comparison used 0.5(sup 0) x 0.5(sup 0) gridded data obtained from the TRMM 3668 product, and similarly gridded GV data obtained from ground-based radars. The comparisons were made at the same spatial and temporal scales in order to eliminate sampling biases in our comparisons. An additional comparison was made by averaging rain rates for the PR, COM and GV estimates within each TMI footprint (approx. 150 square kilometers). For this analysis, unconditional mean rain rates from PR, COM and GV estimates were calculated within each TMI footprint that was observed within 100 km from the respective GV site (and also observed by the PR). This analysis used all the available matching data from the period 1999-2004, representing a sample size of over 50,000 footprints for each site. In the first analysis our results showed that all of the respective rain rate estimates agree well, with some exceptions. The more salient differences were associated with heavy rain events in which one or more of the algorithms failed to properly retrieve these extreme events. Also, it appears that there is a preferred mode of precipitation for TMI rain rates at or near 2 mm per hour over the ocean. This mode was noted over ocean areas of Melbourne, Florida and Kwajalein, Republic of the Marshall Islands, and is shown to exist in TRMM tropical-global ocean areas as well. Further research by algorithm developers is needed to explain or justify the seemingly errant observed probability distributions.

Wolff, David B.↗
NASA NTRS1994

Software for Data Analysis with Graphical Models

Probabilistic graphical models are being used widely in artificial intelligence and statistics, for instance, in diagnosis and expert systems, as a framework for representing and reasoning with probabilities and independencies. They come with corresponding algorithms for performing statistical inference. This offers a unifying framework for prototyping and/or generating data analysis algorithms from graphical specifications. This paper illustrates the framework with an example and then presents some basic techniques for the task: problem decomposition and the calculation of exact Bayes factors. Other tools already developed, such as automatic differentiation, Gibbs sampling, and use of the EM algorithm, make this a broad basis for the generation of data analysis software.

Buntine, Wray L.↗
NASA NTRS1979

TWT design requirements for 30/20 GHz digital communications' satellite

The rapid growth of communication traffic (voice, data, and video) requires the development of additional frequency bands before the 1990's. The frequencies currently in use for satellite communications at 6/4 GHz are crowded and demands for 14/12 GHz systems are increasing. Projections are that these bands will be filled to capacity by the late 1980's. The next higher frequency band allocated for satellite communications is at 30/20 GHz. For interrelated reasons of efficiency, power level, and system reliability criteria, a candidate for the downlink amplifier in a 30/20 GHz communications' satellite is a dual mode traveling wave tube (TWT) equipped with a highly efficient depressed collector. A summary is given of the analyses which determine the TWT design requirements. The overall efficiency of such a tube is then inferred from a parametric study and from experimental data on multistaged depressed collectors. The expected TWT efficiency at 4 dB below output saturation is 24 percent in the high mode and 22 percent in the low mode.

Stankiewicz, N.↗
NASA NTRS2020

The Atacama Cosmology Telescope: DR4 maps and cosmological parameters

We present new arcminute-resolution maps of the Cosmic Microwave Background temperature and polarization anisotropy from the Atacama Cosmology Telescope, using data taken from 2013{2016 at 98 and 150 GHz. The maps cover more than 17,000 deg(^2), the deepest 600 deg(^2) with noise levels below 10µK-arcmin. We use the power spectrum derived from almost 6,000 deg(^2) of these maps to constrain cosmology. The ACT data enable a measurement of the angular scale of features in both the divergence-like polarization and the temperature anisotropy, tracing both the velocity and density at last-scattering. From these one can derive the distance to the last-scattering surface and thus infer the local expansion rate, H0. By combining ACT data with large-scale information from WMAP we measure H0 = 67:6±1:1 km/s/Mpc, at 68% confidence, in excellent agreement with the independently measured Planck satellite estimate (from ACT alone we find H0 = 67:9± 1:5 km/s/Mpc). The ΛCDM model provides a good fit to the ACT data, and we find no evidence for deviations: both the spatial curvature, and the departure from the standard lensing signal in the spectrum, are zero to within 1σ; the number of relativistic species, the primordial Helium fraction, and the running of the spectral index are consistent with ΛCDM predictions to within 1.5{2.2σ. We compare ACT, WMAP, and Planck at the parameter level and find good consistency; we investigate how the constraints on the correlated spectral index and baryon density parameters readjust when adding CMB large-scale information that ACT does not measure. The DR4 products presented here will be publicly released on the NASA Legacy Archive for Microwave Background Data Analysis.

Simone Aiola↗
DOE OSTI2021

How Cooperative Engagement Programs Strengthen Sequencing Capabilities for Biosurveillance and Outbreak Response

The threat of emerging and re-emerging infectious diseases continues to be a challenge to public and global health security. Cooperative biological engagement programs act to build partnerships and collaborations between scientists and health professionals to strengthen capabilities in biosurveillance. Biosurveillance is the systematic process of detecting, reporting, and responding to especially dangerous pathogens and pathogens of pandemic potential before they become outbreaks, epidemics, and pandemics. One important tool in biosurveillance is next generation sequencing. Expensive sequencing machines, reagents, and supplies make it difficult for countries to adopt this technology. Cooperative engagement programs help by providing funding for technical assistance to strengthen sequencing capabilities. Through workshops and training, countries are able to learn sequencing and bioinformatics, and implement these tools in their biosurveillance programs. Cooperative programs have an important role in building and sustaining collaborations among institutions and countries. One of the most important pieces in fostering these collaborations is trust. Trust provides the confidence that a successful collaboration will benefit all parties involved. With sequencing, this enables the sharing of pathogen samples and sequences. Obtaining global sequencing data helps to identify unknown etiological agents, track pathogen evolution and infer transmission networks throughout the duration of a pandemic. Having sequencing technology in place for biosurveillance generates the capacity to provide real-time data to understand and respond to pandemics. We highlight the need for these programs to continue to strengthen sequencing in biosurveillance. By working together to strengthen sequencing capabilities, trust can be formed, benefitting global health in the face of biological threats.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1993

Spectral emissivity of cirrus clouds

The inference of cirrus cloud properties has many important applications including global climate studies, radiation budget determination, remote sensing techniques and oceanic studies from satellites. Data taken at the Parsons Kansas site during the FIRE II project are used for this study. On November 26 there were initially clear sky conditions gradually giving way to a progressively thickening cirrus shield over a period of a few hours. Interferometer radiosonde and lidar data were taken throughout this event. Two techniques are used to infer the downward spectral emittance of the observed cirrus layer. One uses only measurements and the other involves measurements and FASCODE III calculations. FASCODE III is a line-by line radiance/transmittance model developed at the Air Force Geophysics Laboratory.

Beck, Gordon H.↗
NASA NTRS2009

Detection of Subsurface Material Separation in Shuttle Orbiter Slip-Side Joggle Region of the Wing Leading Edge using Infrared Imaging Data from Arc Jet Tests

The objective of the present study was to determine whether infrared imaging (IR) surface temperature data obtained during arc-jet tests of Space Shuttle Orbiter s reinforced carbon-carbon (RCC) wing leading edge panel slip-side joggle region could be used to detect presence of subsurface material separation, and if so, to determine when separation occurs during the simulated entry profile. Recent thermostructural studies have indicated thermally induced interlaminar normal stress concentrations at the substrate/coating interface in the curved joggle region can result in local subsurface material separation, with the separation predicted to occur during approach to peak heating during reentry. The present study was an attempt to determine experimentally when subsurface material separations occur. A simplified thermal model of a flat RCC panel with subsurface material separation was developed and used to infer general surface temperature trends due to the presence of subsurface material separation. IR data from previously conducted arc-jet tests on three test specimens were analyzed: one without subsurface material separation either pre or post test, one with pre test separation, and one with separation developing during test. The simplified thermal model trend predictions along with comparison of experimental IR data of the three test specimens were used to successfully infer material separation from the arc-jet test data. Furthermore, for the test specimen that had developed subsurface material separation during the arc-jet tests, the initiation of separation appeared to occur during the ramp up to the peak heating condition, where test specimen temperature went from 2500 to 2800 F.

Daryabeigi, Kamran↗
NASA NTRS1994

Preliminary solutions for the lunar gravity field from analysis of lunar orbiter tracking data

Knowledge of the gravitation field, in combination with surface topography, provides one of the principal means of inferring the internal structure of a planetary body. Previous analyses of the lunar gravitational field have been based on data from the Lunar Orbiters, the Apollo subsatellites, and the low altitude passes of the Apollo spacecraft. Recently, Konopliv et al. have reanalyzed all available Lunar Orbiter and Apollo subsatellite tracking data, producing a 60th degree and order solution. In preparation for the Clementine Mission to the Moon, we have also initiated a reanalysis of the Lunar Orbiter and Apollo subsatellite data. Our reanalysis takes advantage of advanced force and measurement modeling techniques as well as modern computational facilities. We applied the least squares collocation technique which stabilizes the behavior of the solution and high degree and order. The extension of the size of the field reduces the aliasing coming from the omitted portion of the gravitational field. This is especially important for the analysis of the tracking data from the Lunar Orbiters, as the periapse heights frequently ranged from 50 to 100 km.

Lemoine, F. G.↗