Search NASA⌕ Search

SEARCH · Search NASA

Results for “inference methods”

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

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

At least 1,495 records · Page 83
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Image segmentation with a novel regularized composite shape prior based on surrogate study

Purpose: Incorporating training into image segmentation is a good approach to achieve additional robustness. This work aims to develop an effective strategy to utilize shape prior knowledge, so that the segmentation label evolution can be driven toward the desired global optimum. Methods: In the variational image segmentation framework, a regularization for the composite shape prior is designed to incorporate the geometric relevance of individual training data to the target, which is inferred by an image-based surrogate relevance metric. Specifically, this regularization is imposed on the linear weights of composite shapes and serves as a hyperprior. The overall problem is formulated in a unified optimization setting and a variational block-descent algorithm is derived. Results: The performance of the proposed scheme is assessed in both corpus callosum segmentation from an MR image set and clavicle segmentation based on CT images. The resulted shape composition provides a proper preference for the geometrically relevant training data. A paired Wilcoxon signed rank test demonstrates statistically significant improvement of image segmentation accuracy, when compared to multiatlas label fusion method and three other benchmark active contour schemes. Conclusions: This work has developed a novel composite shape prior regularization, which achieves superior segmentation performance than typical benchmark schemes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Assessing the Application of a Genomic Network Analysis in Population Ecology: Inferring Patterns of Dispersal and Geographic Structure in the Emerging Pathogen, Coccidioides

A challenge in population ecology studies is identifying how to best group individuals into populations, especially when individual origin is unknown. Machine learning has improved upon traditional methods of identifying population structure and is more efficient at handling large, complex datasets. We demonstrate the applicability of a machine learning method to identify hierarchical population structure in an emerging pathogen, Coccidioides spp., the causative agent of Valley fever. We compared the network clusters to structure identified by traditional tools as a validation of the network performance. We used publicly available whole-genome data for 48 C. immitis and 102 C. posadasii, resulting in 168,211 genome-wide SNPs among the two species. The network analysis grouped samples into populations comparable to the literature for these species but also identified fine-scale geographic structure and travel-associated cases not reported thus far. Exploring different resolutions in the network made it easy to identify unique genotypes specific to California and possibly Nevada, as well as Phoenix- and Tucson-acquired infections in non-endemic areas, regardless of reported travel history. The present study provides a promising example of how a ML-based network analysis can improve our ability to understand pathogen ecology, group cases into populations and infer travel-associated infections.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Local orderings in long-range-disordered bismuth-layered intergrowth structure

A series of intergrowth bismuth-layered (Bi{sub 3}TiNbO{sub 9}){sub 2}(Bi{sub 4}Ti{sub 3}O{sub 12}) (2{sub 2}3) ceramics were prepared by conventional solid-state reaction to study the characteristics of the local orderings in long-range-disordered intergrowth structures. High-resolution high-angle annular dark-field (HAADF) imaging reveals the intergrowth structure composed of mixtures of -23-, -223-, -2223- and -22- sequences, while the -223- structure is the thermodynamic stable state of this intergrowth system. It was confirmed by the crystals of recurrent -223- structure prepared by self-flux method and the nature of the local ordering was discussed from their differences in repeating units. The statistics show that when repeating units reach 4 or higher, the independent -223- intergrowth ordering emerges clearly among the competing associated orderings. We infer it is the kinetic factor that induces local compositional variance to result in long-range disordered intergrowth structures. - Graphical abstract: The long-range-disordered intergrowth structure in a (Bi{sub 3}TiNbO{sub 9}){sub 2}(Bi{sub 4}Ti{sub 3}O{sub 12}) (2{sub 2}3) grain, which is composed of various types of local orderings, such as -22-, -23- and -223-. - Highlights: • The characteristic of the long-range-disordered (Bi{sub 3}TiNbO{sub 9}){sub 2}(Bi{sub 4}Ti{sub 3}O{sub 12}) (2{sub 2}3) structure was statistically analyzed, and the ordered -223- structure was speculated to be the thermodynamic stable state of the system. • The crystals of the -223- structure were successfully prepared for the first time by self-melt method. • The lower limit of the repeating units (L) to uniquely determine an independent intergrowth structure was speculated to be L=4. • The analysis inferred that the kinetic process is the controlling factor to limit the structural continuity and induce the long-range-disordered intergrowth structure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Combination of deep neural network with attention mechanism enhances the explainability of protein contact prediction

Abstract Deep learning has emerged as a revolutionary technology for protein residue‐residue contact prediction since the 2012 CASP10 competition. Considerable advancements in the predictive power of the deep learning‐based contact predictions have been achieved since then. However, little effort has been put into interpreting the black‐box deep learning methods. Algorithms that can interpret the relationship between predicted contact maps and the internal mechanism of the deep learning architectures are needed to explore the essential components of contact inference and improve their explainability. In this study, we present an attention‐based convolutional neural network for protein contact prediction, which consists of two attention mechanism‐based modules: sequence attention and regional attention. Our benchmark results on the CASP13 free‐modeling targets demonstrate that the two attention modules added on top of existing typical deep learning models exhibit a complementary effect that contributes to prediction improvements. More importantly, the inclusion of the attention mechanism provides interpretable patterns that contain useful insights into the key fold‐determining residues in proteins. We expect the attention‐based model can provide a reliable and practically interpretable technique that helps break the current bottlenecks in explaining deep neural networks for contact prediction. The source code of our method is available at https://github.com/jianlin-cheng/InterpretContactMap .

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Multifidelity Ensemble Kalman Filtering Using Surrogate Models Defined by Theory-Guided Autoencoders

Data assimilation is a Bayesian inference process that obtains an enhanced understanding of a physical system of interest by fusing information from an inexact physics-based model, and from noisy sparse observations of reality. The multifidelity ensemble Kalman filter (MFEnKF) recently developed by the authors combines a full-order physical model and a hierarchy of reduced order surrogate models in order to increase the computational efficiency of data assimilation. The standard MFEnKF uses linear couplings between models, and is statistically optimal in case of Gaussian probability densities. This work extends the MFEnKF into to make use of a broader class of surrogate model such as those based on machine learning methods such as autoencoders non-linear couplings in between the model hierarchies. We identify the right-invertibility property for autoencoders as being a key predictor of success in the forecasting power of autoencoder-based reduced order models. We propose a methodology that allows us to construct reduced order surrogate models that are more accurate than the ones obtained via conventional linear methods. Numerical experiments with the canonical Lorenz'96 model illustrate that nonlinear surrogates perform better than linear projection-based ones in the context of multifidelity ensemble Kalman filtering. We additionality show a large-scale proof-of-concept result with the quasi-geostrophic equations, showing the competitiveness of the method with a traditional reduced order model-based MFEnKF.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

The ground state and electronic structure of Gd@C{sub 82}: A systematic theoretical investigation of first principle density functionals

As a representative lanthanide endohedral metallofullerene, Gd@C{sub 82} has attracted a widespread attention among theorists and experimentalists ever since its first synthesis. Through comprehensive comparisons and discussions, as well as references to the latest high precision experiments, we evaluated the performance of different computational methods. Our results showed that the appropriate choice of the exchange-correlation functionals is the decisive factor to accurately predict both geometric and electronic structures for Gd@C{sub 82}. The electronic structure of the ground state and energy gap between the septet ground state and the nonet low-lying state obtained from pure density functional methods, such as PBE and PW91, are in good agreement with current experiment. Unlike pure functionals, the popularly used hybrid functionals in previous studies, such as B3LYP, could infer the qualitative correct ground state only when small basis set for C atoms is employed. Furthermore, we also highlighted that other geometric structures of Gd@C{sub 82} with the Gd staying at different positions are either not stable or with higher energies. This work should provide some useful references for various theoretical methodologies in further density functional studies on Gd@C{sub 82} and its derivatives in the future.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1993

ERS-1 and Seasat scatterometer measurements of ocean winds: Model functions and the directional distribution of short waves

Calculation of accurate vector winds from scatterometers requires knowledge of the relationship between backscatter cross-section and the geophysical variable of interest. As the detailed dynamics of wind generation of centimetric waves and radar-sea surface scattering at moderate incidence angles are not well known, empirical scatterometer model functions relating backscatter to winds must be developed. Less well appreciated is the fact that, given an accurate model function and some knowledge of the dominant scattering mechanisms, significant information on the amplitudes and directional distributions of centimetric roughness elements on the sea surface can be inferred. accurate scatterometer model functions can thus be used to investigate wind generation of short waves under realistic conditions. The present investigation involves developing an empirical model function for the C-band (5.3 GHz) ERS-1 scatterometer and comparing Ku-band model functions with the C-band model to infer information on the two-dimensional spectrum of centimetric roughness elements in the ocean. The C-band model function development is based on collocations of global backscatter measurements with operational surface analyses produced by meteorological agencies. Strengths and limitations of the method are discussed, and the resulting model function is validated in part through comparison with the actual distributions of backscatter cross-section triplets. Details of the directional modulation as well as the wind speed sensitivity at C-band are investigated. Analysis of persistent outliers in the data is used to infer the magnitudes of non-wind effects (such as atmospheric stratification, swell, etc.). The ERS-1 C-band instrument and the Seasat Ku-band (14.6 GHz) scatterometer both imaged waves of approximately 3.4 cm wavelength assuming that Bragg scattering is the dominant mechanism. Comparisons of the C-band and Ku-band model functions are used both to test the validity of the postulated Bragg mechanism and to investigate the directional distribution of the imaged waves under a variety of conditions where Bragg scatter is dominant.

Freilich, Michael H.↗
DOE OSTI2025

Real-time inference and extrapolation with Time-Conditioned UNet: Applications in hypersonic flows, incompressible flows, and global temperature forecasting

Neural Operators are fast and accurate surrogates for nonlinear mappings between functional spaces within training domains. Extrapolation beyond the training domain remains a grand challenge across all application areas. We present Time-Conditioned UNet (TC-UNet) as an operator learning method to solve time-dependent PDEs continuously in time without any temporal discretization, including in extrapolation scenarios. TC-UNet incorporates the temporal evolution of the PDE into its architecture by combining a parameter conditioning approach with the attention mechanism from the Transformer architecture. After training, TC-UNet makes real-time inferences on an arbitrary temporal grid. We demonstrate its extrapolation capability on a climate problem by estimating the global temperature for several years and also for inviscid hypersonic flow around a double cone. We propose different training strategies involving temporal bundling and sub-sampling. We demonstrate performance improvements for several benchmarks, performing extrapolation for long time intervals and zero-shot super-resolution time.

Deep learning↗
DOE OSTI2015

Improved Sampling Algorithms in the Risk-Informed Safety Margin Characterization Toolkit

The RISMC approach is developing advanced set of methodologies and algorithms in order to perform Probabilistic Risk Analyses (PRAs). In contrast to classical PRA methods, which are based on Event-Tree and Fault-Tree methods, the RISMC approach largely employs system simulator codes applied to stochastic analysis tools. The basic idea is to randomly perturb (by employing sampling algorithms) timing and sequencing of events and internal parameters of the system codes (i.e., uncertain parameters) in order to estimate stochastic parameters such as core damage probability. This approach applied to complex systems such as nuclear power plants requires to perform a series of computationally expensive simulation runs given a large set of uncertain parameters. These types of analysis are affected by two issues. Firstly, the space of the possible solutions (a.k.a., the issue space or the response surface) can be sampled only very sparsely, and this precludes the ability to fully analyze the impact of uncertainties on the system dynamics. Secondly, large amounts of data are generated and tools to generate knowledge from such data sets are not yet available. This report focuses on the first issue and in particular employs novel methods that optimize the information generated by the sampling process by sampling unexplored and risk-significant regions of the issue space: adaptive (smart) sampling algorithms. They infer system response from surrogate models constructed from existing samples and predict the most relevant location of the next sample. It is therefore possible to understand features of the issue space with a small number of carefully selected samples. In this report, we will present how it is possible to perform adaptive sampling using the RISMC toolkit and highlight the advantages compared to more classical sampling approaches such Monte-Carlo. We will employ RAVEN to perform such statistical analyses using both analytical cases but also another RISMC code: RELAP-7.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS2021

Conjugate Photoelectron Energy Spectra Derived From Coincident FUV and Radio Measurements

We present a method for estimating incident photoelectrons' energy spectra as a function of altitude by combining global scale far-ultraviolet (FUV) and radio-occultation (RO) measurements. This characterization provides timely insights important for accurate interpretation of ionospheric parameters inferred from the recently launched Ionospheric Connection Explorer (ICON) observations. Quantification of photoelectron impact is enabled by the fact that conjugate photoelectrons (CPEs) directly affect FUV airglow emissions but not RO measurements. We demonstrate a technique for estimation of photoelectron fluxes and their spectra by combining coincident ICON and COSMIC2 measurements and show that a significant fraction of ICON-FUV measurements is affected by CPEs during the winter solstice. A comparison of estimated photoelectron fluxes with measured photoelectron spectra is used to gain further insights into the estimation method and reveals consistent values within 10–60 eV. Plain Language Summary The impact of solar radiation on the atmosphere produces highly energetic electrons, which travel freely along the magnetic Earth's field lines from one hemisphere to the other. When these electrons flow from the sunlit side into the nightside hemisphere, they interact with the neutral species and produce noticeable effects in the ionosphere such as an increase in electron temperature and enhancement of airglow emissions. This study presents a method to quantify the amount of precipitating electrons and their energy on a global scale using two recent satellite missions, ICON and COSMIC2. Our results demonstrate that coincident far-ultraviolet (ICON) and radio-occultation (COSMIC2) measurements from space are valuable resources to study precipitating electrons in the ionosphere and their impact on inferring ionospheric plasma parameters.

J M Urco↗
NASA NTRSDate not supplied

MEDLI2: MISP Inferred Aerothermal Environment and Flow Transition Assessment

The Mars Entry, Descent, and Landing Instrumentation 2 (MEDLI2) sensor suite on the Mars2020 mission contained multiple sensors on the aeroshell to measure the aerothermal environment during entry into the Martian atmosphere. These sensors performed superbly and successfully returned forebody and aftbody heating measurements. Analysis of MEDLI2 data indicated flow transitioning from a laminar to turbulent state on the heatshield. No evidence of flow transition was observed on the backshell. Two methods were used to estimate flow transition times on the heatshield: (1) temperature gradient of near-surface thermocouple data and (2) heat flux gradient from an inverse reconstruction approach using thermocouple data and material response modeling. Both methods produced similar transition times with an estimated accuracy of ±1 s. To assess various transition criteria, transition parameters were evaluated at each sensor location using flow field solutions from computational fluid dynamics (CFD) simulations. The idea was to use conservative values inferred from MEDLI2 data as transition criteria for other Mars missions. To test this hypothesis, MEDLI data from the Mars Science Laboratory (MSL) mission was used to compare predicted vs. actual flow transition times. The comparisons suggest smooth wall transition criteria are not well-suited in modeling the rapid progression of a turbulent transition front. Transition criteria containing a roughness element parameter agreed better with the flight data. In summary, critical transition values derived from MEDLI2 data may be used as a starting point in constructing a flow transition model for future Mars missions.

Chun Y Tang↗
NASA NTRS2006

Using the Bootstrap Method for a Statistical Significance Test of Differences between Summary Histograms

A new method is proposed to compare statistical differences between summary histograms, which are the histograms summed over a large ensemble of individual histograms. It consists of choosing a distance statistic for measuring the difference between summary histograms and using a bootstrap procedure to calculate the statistical significance level. Bootstrapping is an approach to statistical inference that makes few assumptions about the underlying probability distribution that describes the data. Three distance statistics are compared in this study. They are the Euclidean distance, the Jeffries-Matusita distance and the Kuiper distance. The data used in testing the bootstrap method are satellite measurements of cloud systems called cloud objects. Each cloud object is defined as a contiguous region/patch composed of individual footprints or fields of view. A histogram of measured values over footprints is generated for each parameter of each cloud object and then summary histograms are accumulated over all individual histograms in a given cloud-object size category. The results of statistical hypothesis tests using all three distances as test statistics are generally similar, indicating the validity of the proposed method. The Euclidean distance is determined to be most suitable after comparing the statistical tests of several parameters with distinct probability distributions among three cloud-object size categories. Impacts on the statistical significance levels resulting from differences in the total lengths of satellite footprint data between two size categories are also discussed.

Xu, Kuan-Man↗
DOE OSTI2023

Inferring the photolysis rate of NO 2 in the stratosphere based on satellite observations

NO and NO 2 (NO x ) play major roles in both tropospheric and stratospheric chemistry. This paper provides a novel method to obtain a global and accurate photolysis rate for NO 2 based on satellite data. The photolysis rate J(NO 2 ) dominates the daytime diurnal variation of NO x photochemistry. Here the spatial variation of J(NO 2 ) at 50–90°S in December from 20–40km is obtained using data from the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) experiment. Because NO and NO 2 rapidly exchange with one another in the daytime, J(NO 2 ) can be attained assuming steady state, and the results are shown to be consistent with model results. The J(NO 2 ) value decreases as the solar zenith angle increases and has a weak altitude dependence. A key finding is that satellite-derived J(NO 2 ) increases in the polar regions, in good agreement with model predictions, due to the effects of ice and snow on surface albedo. Thus, the method presented here provides an observation-based check on the role of albedo in driving polar photochemistry.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2011

Pulsed Electric Propulsion Thrust Stand Calibration Method

The evaluation of the performance of any propulsion device requires the accurate measurement of thrust. While chemical rocket thrust is typically measured using a load cell, the low thrust levels associated with electric propulsion (EP) systems necessitate the use of much more sensitive measurement techniques. The design and development of electric propulsion thrust stands that employ a conventional hanging pendulum arm connected to a balance mechanism consisting of a secondary arm and variable linkage have been reported in recent publications by Polzin et al. These works focused on performing steady-state thrust measurements and employed a static analysis of the thrust stand response. In the present work, we present a calibration method and data that will permit pulsed thrust measurements using the Variable Amplitude Hanging Pendulum with Extended Range (VAHPER) thrust stand. Pulsed thrust measurements are challenging in general because the pulsed thrust (impulse bit) occurs over a short timescale (typically 1 micros to 1 millisecond) and cannot be resolved directly. Consequently, the imparted impulse bit must be inferred through observation of the change in thrust stand motion effected by the pulse. Pulsed thrust measurements have typically only consisted of single-shot operation. In the present work, we discuss repetition-rate pulsed thruster operation and describe a method to perform these measurements. The thrust stand response can be modeled as a spring-mass-damper system with a repetitive delta forcing function to represent the impulsive action of the thruster.

Wong, Andrea R.↗
NASA NTRS2009

A Method for Retrieving Ground Flash Fraction from Satellite Lightning Imager Data

A general theory for retrieving the fraction of ground flashes in N lightning observed by a satellite-based lightning imager is provided. An "exponential model" is applied as a physically reasonable constraint to describe the measured optical parameter distributions, and population statistics (i.e., mean, variance) are invoked to add additional constraints to the retrieval process. The retrieval itself is expressed in terms of a Bayesian inference, and the Maximum A Posteriori (MAP) solution is obtained. The approach is tested by performing simulated retrievals, and retrieval error statistics are provided. The ability to retrieve ground flash fraction has important benefits to the atmospheric chemistry community. For example, using the method to partition the existing satellite global lightning climatology into separate ground and cloud flash climatologies will improve estimates of lightning nitrogen oxides (NOx) production; this in turn will improve both regional air quality and global chemistry/climate model predictions.

Koshak, William J.↗
DOE OSTI2017

astroABC : An Approximate Bayesian Computation Sequential Monte Carlo sampler for cosmological parameter estimation

Given the complexity of modern cosmological parameter inference where we arefaced with non-Gaussian data and noise, correlated systematics and multi-probecorrelated data sets, the Approximate Bayesian Computation (ABC) method is apromising alternative to traditional Markov Chain Monte Carlo approaches in thecase where the Likelihood is intractable or unknown. The ABC method is called"Likelihood free" as it avoids explicit evaluation of the Likelihood by using aforward model simulation of the data which can include systematics. Weintroduce astroABC, an open source ABC Sequential Monte Carlo (SMC) sampler forparameter estimation. A key challenge in astrophysics is the efficient use oflarge multi-probe datasets to constrain high dimensional, possibly correlatedparameter spaces. With this in mind astroABC allows for massive parallelizationusing MPI, a framework that handles spawning of jobs across multiple nodes. Akey new feature of astroABC is the ability to create MPI groups with differentcommunicators, one for the sampler and several others for the forward modelsimulation, which speeds up sampling time considerably. For smaller jobs thePython multiprocessing option is also available. Other key features include: aSequential Monte Carlo sampler, a method for iteratively adapting tolerancelevels, local covariance estimate using scikit-learn's KDTree, modules forspecifying optimal covariance matrix for a component-wise or multivariatenormal perturbation kernel, output and restart files are backed up everyiteration, user defined metric and simulation methods, a module for specifyingheterogeneous parameter priors including non-standard prior PDFs, a module forspecifying a constant, linear, log or exponential tolerance level,well-documented examples and sample scripts. This code is hosted online athttps://github.com/EliseJ/astroABC

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Genome-scale phylogenetics reveals a monophyletic Zoopagales (Zoopagomycota, Fungi)

Previous genome-scale phylogenetic analyses of Fungi have under sampled taxa from Zoopagales; this order contains many predacious or parasitic genera, and most have never been grown in pure culture. We sequenced the genomes of 4 zoopagalean taxa that are predators of amoebae, nematodes, or rotifers and the genome of one taxon that is a parasite of amoebae using single cell sequencing methods with whole genome amplification. Each genome was a metagenome, which was assembled and binned using multiple techniques to identify the target genomes. We inferred phylogenies with both super matrix and coalescent approaches using 192 conserved proteins mined from the target genomes and performed ancestral state reconstructions to determine the ancestral trophic lifestyle of the clade. Our results indicate that Zoopagales is monophyletic. Ancestral state reconstructions provide moderate support for mycoparasitism being the ancestral state of the clade.

59 BASIC BIOLOGICAL SCIENCES↗