Search NASA⌕ Search

SEARCH · Search NASA

Results for “deep data”

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 1,441 records · Page 80
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Barometric Pumping Through Fractured Rock: A Mechanism for Venting Deep Methane to Mars' Atmosphere

Abstract Both the source of methane on Mars and the mechanism for transmission from the subsurface to the atmosphere are not fully understood. Previous seepage simulations have invoked relatively shallow subsurface sources to explain observed methane signatures on Mars. We propose that barometric‐pressure pumping through fracture networks could be an effective mechanism for methane transport from the deep subsurface on Mars. Using atmospheric pressure data gathered by Curiosity as input, we simulate methane gas transport from depths of 200 m to the surface. Even with such a deep source, our model reproduces the observed seasonality of methane, and the simulated surface methane fluxes fall within the range of previous estimates derived from atmospheric observations. Because 200 m is the likely minimum hospitable depth for living methanogenic microbes, our fracture network model indirectly reinvigorates the possibility of a microbial source of methane on Mars.

58 GEOSCIENCES↗
DOE OSTI2018

A record of deep-ocean dissolved O 2 from the oxidation state of iron in submarine basalts

The oxygenation of the deep ocean in the geological past has been associated with a rise in the partial pressure of atmospheric molecular oxygen (O 2 ) to near-present levels and the emergence of modern marine biogeochemical cycles. It has also been linked to the origination and diversification of early animals. It is generally thought that the deep ocean was largely anoxic from about 2,500 to 800 million years ago, with estimates of the occurrence of deep-ocean oxygenation and the linked increase in the partial pressure of atmospheric oxygen to levels sufficient for this oxygenation ranging from about 800 to 400 million years ago. Deep-ocean dissolved oxygen concentrations over this interval are typically estimated using geochemical signatures preserved in ancient continental shelf or slope sediments, which only indirectly reflect the geochemical state of the deep ocean. Here we present a record that more directly reflects deep-ocean oxygen concentrations, based on the ratio of Fe 3+ to total Fe in hydrothermally altered basalts formed in ocean basins. Our data allow for quantitative estimates of deep-ocean dissolved oxygen concentrations from 3.5 billion years ago to 14 million years ago and suggest that deep-ocean oxygenation occurred in the Phanerozoic (541 million years ago to the present) and potentially not until the late Palaeozoic (less than 420 million years ago).

58 GEOSCIENCES↗
NASA NTRS2009

DSN Data Visualization Suite

The DSN Data Visualization Suite is a set of computer programs and reusable Application Programming Interfaces (APIs) that assist in the visualization and analysis of Deep Space Network (DSN) spacecraft-tracking data, which can include predicted and actual values of downlink frequencies, uplink frequencies, and antenna-pointing angles in various formats that can include tables of values and polynomial coefficients. The data can also include lists of antenna-pointing events, lists of antenna- limit events, and schedules of tracking activities. To date, analysis and correlation of these intricately related data before and after tracking have been difficult and time-consuming. The DSN Data Visualization Suite enables operators to quickly diagnose tracking-data problems before, during, and after tracking. The Suite provides interpolation on demand and plotting of DSN tracking data, correlation of all data on a given temporal point, and display of data with color coding configurable by users. The suite thereby enables rapid analysis of the data prior to transmission of the data to DSN control centers. At the control centers, the same suite enables operators to validate the data before committing the data to DSN subsystems. This software is also Web-enabled to afford its capabilities to international space agencies.

Bui, Bach X.↗
DOE OSTI2022

Constraining the nuclear gluon PDF with inclusive hadron production data

The nuclear parton distribution functions (nPDFs) of gluons are known to be difficult to determine with fits of deep inelastic scattering (DIS) and Drell-Yan (DY) data alone. Therefore, the nCTEQ15 analysis of nuclear PDFs added inclusive neutral pion production data from RHIC to help in constraining the gluon. In this analysis, we present a new global analysis of nuclear PDFs based on a much larger set of single inclusive light hadron data from RHIC and the LHC. Using our new nCTEQ code (nCTEQ++) with an optimized version of INCNLO we study systematically the limitations of the theory and the impact of the fragmentation function uncertainty.

Duwentäster, Pit↗
DOE OSTI2022

Updated QCD global analysis of single transverse-spin asymmetries: Extracting $\tilde{H}$, and the role of the Soffer bound and lattice QCD

Here, we present an update to the QCD global analysis of single transverse-spin asymmetries presented in [J. Cammarota et al. (Jefferson Lab Angular Momentum Collaboration), Phys. Rev. D 102, 054002 (2020).] (JAM3D-20). JAM3D-20 simultaneously included transverse momentum dependent and collinear twist-3 observables, both of which are sensitive to quark-gluon-quark correlations in hadrons. In this study we extract for the first time the twist-3 chiral odd fragmentation function $\tilde{H}$ by incorporating the sin φ s modulation data from semi-inclusive deep-inelastic scattering along with its contribution to the single transverse-spin asymmetry in pion production from proton-proton collisions. We also explore the impact of lattice QCD tensor charge calculations and the Soffer bound on our global analysis. We find that both constraints can be accommodated within our results, with $\tilde{H}$ playing a key role in maintaining agreement with the data from proton-proton collisions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Exploration of Domain Aware Machine Learning for Grid Analytics: Transfer-Learnt Energy Models to Assist Buildings Control with Sparse Field Data

Buildings are a primary consumer of energy in the United States and are also increasingly being perceived as providers of grid services such as load shifting, shedding and modulation. High fidelity models of building energy consumption are needed to set appropriate baselines for measurement and verification (M&V) of controllers designed for energy efficient operation of buildings and to enable buildings to provide grid services via. participation in demand response programs. State-of-the-art building energy modeling techniques either rely on Physics based models, or extensive instrumentation of the building envelope to gather “big” data to train machine learning based models such as deep neural networks. While Physics based models are often limited by their accuracy, it is not always feasible to gather a significant amount of field data required to train machine learning based models with sufficient accuracy. In this paper, we explore the use of transfer learning-based strategies to address unsatisfactory accuracy of models for estimating building energy consumption when available field data for training is sparse or of unacceptable quality. In particular, we transfer knowledge in the form of data and parameters, from Physics based simulation frameworks to the field to improve the model accuracy, thus resulting in a Physics-informed Machine Learning framework. We evaluated the efficacy of our approach on field data collected from six commercial buildings and our results indicate that the proposed transfer learning based models provide comparative (and in some cases better) accuracy than state-of-the-art machine learning and deep learning solutions, with just one month of field data.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Evaluating the Potential and Challenges of an Uncertainty Quantification Method for Long Short–Term Memory Models for Soil Moisture Predictions

Recently, recurrent deep networks have shown promise to harness newly available satellite–sensed data for long–term soil moisture projections. However, to be useful in forecasting, deep networks must also provide uncertainty estimates. Here we evaluated Monte Carlo dropout with an input–dependent data noise term (MCD+N), an efficient uncertainty estimation framework originally developed in computer vision, for hydrologic time series predictions. MCD+N simultaneously estimates a heteroscedastic input–dependent data noise term (a trained error model attributable to observational noise) and a network weight uncertainty term (attributable to insufficiently constrained model parameters). Although MCD+N has appealing features, many heuristic approximations were employed during its derivation, and rigorous evaluations and evidence of its asserted capability to detect dissimilarity were lacking. To address this, we provided an in–depth evaluation of the scheme's potential and limitations. We showed that for reproducing soil moisture dynamics recorded by the Soil Moisture Active Passive (SMAP) mission, MCD+N indeed gave a good estimate of predictive error, provided that we tuned a hyperparameter and used a representative training data set. The input–dependent term responded strongly to observational noise, while the model term clearly acted as a detector for physiographic dissimilarity from the training data, behaving as intended. However, when the training and test data were characteristically different, the input–dependent term could be misled, undermining its reliability. Additionally, due to the data–driven nature of the model, data noise also influences network weight uncertainty, and therefore the two uncertainty terms are correlated. Altogether, this approach has promise, but care is needed to interpret the results.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2010

Shallow and Deep Latent Heating Modes Over Tropical Oceans Observed with TRMM PR Spectral Latent Heating Data

The global hydrological cycle is central to the Earth's climate system, with rainfall and the physics of its formation acting as the key links in the cycle. Two-thirds of global rainfall occurs in the Tropics. Associated with this rainfall is a vast amount of heat, which is known as latent heat. It arises mainly due to the phase change of water vapor condensing into liquid droplets; three-fourths of the total heat energy available to the Earth's atmosphere comes from tropical rainfall. In addition, fresh water provided by tropical rainfall and its variability exerts a large impact upon the structure and motions of the upper ocean layer. Three-dimensional distributions of latent heating estimated from Tropical Rainfall Measuring Mission Precipitation Radar (TRMM PR)utilizing the Spectral Latent Heating (SLH) algorithm are analyzed. Mass-weighted and vertically integrated latent heating averaged over the tropical oceans is estimated as approx.72.6 J/s (approx.2.51 mm/day), and that over tropical land is approx.73.7 J/s (approx.2.55 mm/day), for 30degN-30degS. It is shown that non-drizzle precipitation over tropical and subtropical oceans consists of two dominant modes of rainfall systems, deep systems and congestus. A rough estimate of shallow mode contribution against the total heating is about 46.7 % for the average tropical oceans, which is substantially larger than 23.7 % over tropical land. While cumulus congestus heating linearly correlates with the SST, deep mode is dynamically bounded by large-scale subsidence. It is notable that substantial amount of rain, as large as 2.38 mm day-1 in average, is brought from congestus clouds under the large-scale subsiding circulation. It is also notable that even in the region with SST warmer than 28 oC, large-scale subsidence effectively suppresses the deep convection, remaining the heating by congestus clouds. Our results support that the entrainment of mid-to-lower-tropospheric dry air, which accompanies the large-scale subsidence is the major factor suppressing the deep convection. Therefore, representation of the realistic entrainment is very important for proper reproduction of precipitation distribution and resultant large-scale circulation.

Takayabu, Yukari N.↗
NASA NTRS1986

The Telecommunications and Data Acquisition Report

This publication, one of a series formerly titled The Deep Space Network (DSN) Progress Report, documents DSN progress in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations. In addition, developments in Earth-based radio technology as applied to geodynamics, astrophysics, and the radio search for extraterrestrial intelligence are reported.

Posner, E. C.↗
DOE OSTI2021

Mitigating Catastrophic Forgetting in Deep Learning in a Streaming Setting Using Historical Summary

Recent advancements in scientific equipment and the adaptation of electronics and the Internet of Things (IoT) in our everyday lives resulted in large and complex data production at a high rate. Making meaningful and timely knowledge discovery at a modest cost from this big data is difficult for computing power and storage limitations. Training deep learning models incrementally in a streaming setting can help us with overcoming these limitations. However, in a well-known phenomenon named catastrophic forgetting, incrementally trained models increasingly perform poorly on the past data. To mitigate catastrophic forgetting in training in a streaming setting, we propose constructing a historical summary over time and use the summary with newly arrived data during incremental training. We propose various data summarization techniques such as random sampling, micro clustering, coreset computation, and Auto Encoders to counteract catastrophic forgetting. We built a pipeline for incremental training with a historical summary for training deep learning models for streaming data. We demonstrate the effectiveness of historical summary in mitigating catastrophic forgetting using three case studies involving three different deep learning applications: an Artificial Neural Network (ANN) for classification task on MNIST dataset, a language model (RNN-LM) on the WikiText2 dataset, and a Convolutional Neural Network (CNN), ResNet50 to classify the ImageNet dataset. Through the training of the models, we observe that catastrophic forgetting is evident in ANN and CNN but not in an RNN. For the first task, our method recovers up to 47.9% lost accuracy due to catastrophic forgetting. For the third task, the historical summary recovers classification accuracy by up to 25%. For the second task, though there is not proof of catastrophic forgetting, the training performance (PPL) improves by up to 26% with historical summary.

Dash, Sajal↗
DOE OSTI2016

Carbonate “clumped” isotope signatures in aragonitic scleractinian and calcitic gorgonian deep-sea corals

Deep-sea corals are a potentially valuable archive of the temperature and ocean chemistry of intermediate and deep waters. Living in near-constant temperature, salinity, and pH and having amongst the slowest calcification rates observed in carbonate-precipitating biological organisms, deep-sea corals can provide valuable constraints on processes driving mineral equilibrium and disequilibrium isotope signatures. Here we report new data to further develop “clumped” isotopes as a paleothermometer in deep-sea corals as well as to investigate mineral-specific, taxon-specific, and growth-rate-related effects. Carbonate clumped isotope thermometry is based on measurements of the abundance of the doubly substituted isotopologue 13 C 18 O 16 O 2 in carbonate minerals, analyzed in CO 2 gas liberated on phosphoric acid digestion of carbonates and reported as Δ 47 values. We analyzed Δ 47 in live-collected aragonitic scleractinian ( Enallopsammia sp.) and high-Mg calcitic gorgonian (Isididae and Coralliidae) deep-sea corals and compared results to published data for other aragonitic scleractinian taxa. Measured Δ 47 values were compared to in situ temperatures, and the relationship between Δ 47 and temperature was determined for each group to investigate taxon-specific effects. We find that aragonitic scleractinian deep-sea corals exhibit higher values than high-Mg calcitic gorgonian corals and the two groups of coral produce statistically different relationships between Δ 47 –temperature calibrations. These data are significant in the interpretation of all carbonate clumped isotope calibration data as they show that distinct Δ 47 –temperature calibrations can be observed in different materials recovered from the same environment and analyzed using the same instrumentation, phosphoric acid composition, digestion temperature and technique, CO 2 gas purification apparatus, and data handling. There are three possible explanations for the origin of these different calibrations. The offset between the corals of different mineralogy is in the same direction as published theoretical predictions for the offset between calcite and aragonite although the magnitude of the offset is different. One possibility is that the deep-sea coral results reflect high-Mg and aragonite crystals attaining nominal mineral equilibrium clumped isotope signatures due to conditions of extremely slow growth. In that case, a possible explanation for the attainment of disequilibrium bulk isotope signatures and equilibrium clumped isotope signatures by deep-sea corals is that extraordinarily slow growth rates can promote the occurrence of isotopic reordering in the interfacial region of growing crystals. We also cannot rule out a component of a biological “vital effect” influencing clumped isotope signatures in one or both orders of coral. Based on published experimental data and theoretical calculations, these biological vital effects could arise from kinetic isotope effects due to the source of carbon used for calcification, temperature- and pH-dependent rates of CO 2 hydration and/or hydroxylation, calcifying fluid pH, the activity of carbonic anhydrase, the residence time of dissolved inorganic carbon in the calcifying fluid, and calcification rate. A third possible explanation is the occurrence of variable acid digestion fractionation factors. Although a recent study has suggested that dolomite, calcite, and aragonite may have similar clumped isotope acid digestion fractionation factors, the influence of acid digestion kinetics on Δ 47 is a subject that warrants further investigation.

58 GEOSCIENCES↗
NASA NTRS2012

Automated Performance Characterization of DSN System Frequency Stability Using Spacecraft Tracking Data

This software provides an automated capability to measure and qualify the frequency stability performance of the Deep Space Network (DSN) ground system, using daily spacecraft tracking data. The results help to verify if the DSN performance is meeting its specification, therefore ensuring commitments to flight missions; in particular, the radio science investigations. The rich set of data also helps the DSN Operations and Maintenance team to identify the trends and patterns, allowing them to identify the antennas of lower performance and implement corrective action in a timely manner. Unlike the traditional approach where the performance can only be obtained from special calibration sessions that are both time-consuming and require manual setup, the new method taps into the daily spacecraft tracking data. This new approach significantly increases the amount of data available for analysis, roughly by two orders of magnitude, making it possible to conduct trend analysis with good confidence. The software is built with automation in mind for end-to-end processing. From the inputs gathering to computation analysis and later data visualization of the results, all steps are done automatically, making the data production at near zero cost. This allows the limited engineering resource to focus on high-level assessment and to follow up with the exceptions/deviations. To make it possible to process the continual stream of daily incoming data without much effort, and to understand the results quickly, the processing needs to be automated and the data summarized at a high level. Special attention needs to be given to data gathering, input validation, handling anomalous conditions, computation, and presenting the results in a visual form that makes it easy to spot items of exception/ deviation so that further analysis can be directed and corrective actions followed.

Pham, Timothy T.↗
NASA NTRS2012

Automated Performance Characterization of DSN System Frequency Stability Using Spacecraft Tracking Data

This software provides an automated capability to measure and qualify the frequency stability performance of the Deep Space Network (DSN) ground system, using daily spacecraft tracking data. The results help to verify if the DSN performance is meeting its specification, therefore ensuring commitments to flight missions; in particular, the radio science investigations. The rich set of data also helps the DSN Operations and Maintenance team to identify the trends and patterns, allowing them to identify the antennas of lower performance and implement corrective action in a timely manner. Unlike the traditional approach where the performance can only be obtained from special calibration sessions that are both time-consuming and require manual setup, the new method taps into the daily spacecraft tracking data. This new approach significantly increases the amount of data available for analysis, roughly by two orders of magnitude, making it possible to conduct trend analysis with good confidence. The software is built with automation in mind for end-to-end processing. From the inputs gathering to computation analysis and later data visualization of the results, all steps are done automatically, making the data production at near zero cost. This allows the limited engineering resource to focus on high-level assessment and to follow up with the exceptions/deviations. To make it possible to process the continual stream of daily incoming data without much effort, and to understand the results quickly, the processing needs to be automated and the data summarized at a high level. Special attention needs to be given to data gathering, input validation, handling anomalous conditions, computation, and presenting the results in a visual form that makes it easy to spot items of exception/deviation so that further analysis can be directed and corrective actions followed.

Pham, Timothy T.↗
DOE OSTI2026

Machine learning for seismic low-frequency extrapolation

The cycle-skipping problem that plagues full waveform inversion (FWI) can be at least partially mitigated if low frequencies (which encode the kinematics of wave propagation in seismic data) are recorded. However, seismic sources and receivers are band-limited, so seismic data does not generally include signals down to 0 Hz. To improve our ability to solve the seismic inverse problem, one can synthesize this missing low-frequency (LF) content from the recorded high-frequency (HF) data using machine learning (ML) models. Deep learning models such as convolutional neural networks (CNNs) demonstrate impressive ability to perform low frequency extrapolation. However, such models require powerful hardware (GPU machines) and careful training. We assess the extrapolation capabilities of three different ML models that do not require GPU machines, namely, random forest, Gaussian process regression and gradient boosting, on both synthetic and real data. Experimental results on two synthetic data sets (generated from a low velocity lens embedded in a homogeneous medium, and the Marmousi model) demonstrate that FWI applied to the extrapolated data consistently improves inversion accuracy relative to FWI applied to the original data sets that do not contain low frequencies. Application of low-frequency extrapolation to real data from the Northwest Shelf of Australia demonstrates that tree-based ML models such as gradient boosting can outperform CNNs in terms of both accuracy and computational cost on non-GPU architectures.

58 GEOSCIENCES↗
DOE OSTI2023

Strictly Enforcing Invertibility and Conservation in CNN-Based Super Resolution for Scientific Datasets

Abstract Recently, deep convolutional neural networks (CNNs) have revolutionized image “super resolution” (SR), dramatically outperforming past methods for enhancing image resolution. They could be a boon for the many scientific fields that involve imaging or any regularly gridded datasets: satellite remote sensing, radar meteorology, medical imaging, numerical modeling, and so on. Unfortunately, while SR-CNNs produce visually compelling results, they do not necessarily conserve physical quantities between their low-resolution inputs and high-resolution outputs when applied to scientific datasets. Here, a method for “downsampling enforcement” in SR-CNNs is proposed. A differentiable operator is derived that, when applied as the final transfer function of a CNN, ensures the high-resolution outputs exactly reproduce the low-resolution inputs under 2D-average downsampling while improving performance of the SR schemes. The method is demonstrated across seven modern CNN-based SR schemes on several benchmark image datasets, and applications to weather radar, satellite imager, and climate model data are shown. The approach improves training time and performance while ensuring physical consistency between the super-resolved and low-resolution data. Significance Statement Recent advancements in using deep learning to increase the resolution of images have substantial potential across the many scientific fields that use images and image-like data. Most image super-resolution research has focused on the visual quality of outputs, however, and is not necessarily well suited for use with scientific data where known physics constraints may need to be enforced. Here, we introduce a method to modify existing deep neural network architectures so that they strictly conserve physical quantities in the input field when “super resolving” scientific data and find that the method can improve performance across a wide range of datasets and neural networks. Integration of known physics and adherence to established physical constraints into deep neural networks will be a critical step before their potential can be fully realized in the physical sciences.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Toward a Generalizable Prediction Model of Molten Salt Mixture Density with Chemistry‐Informed Transfer Learning

Optimally designing applications of molten salts requires knowledge of their thermophysical properties over a wide range of temperatures and compositions. There exist significant gaps in existing databases and this data can be challenging to experimentally measure due to high temperatures, salt corrosivity, and salt hygroscopicity. Existing databases have been used to create Redlich–Kister (RK) models for mixture density showing improved accuracy with respect to ideal mixing assumptions, but these models require subcomponent data measurements for each new system, therefore lacking generality. In order to address generalizability and data sparsity, a transfer learning procedure is proposed to train deep neural networks (DNNs) using a combination of semi‐empirical relationships (RK), data from the thermophysical arm of the molten salt thermal properties database and universal ab initio properties of component mixtures taken from the joint automated repository for various integrated simulations (JARVIS) classical force‐field inspired descriptors database to predict density in molten salts. Herein, it is shown that DNNs predict molten salt density with an r 2 over 0.99 and a mean absolute percentage error under 1%, outperforming alternative methods.

inorganic materials↗
DOE OSTI2024

AI-NERD: Elucidation of relaxation dynamics beyond equilibrium through AI-informed X-ray photon correlation spectroscopy

Abstract Understanding and interpreting dynamics of functional materials in situ is a grand challenge in physics and materials science due to the difficulty of experimentally probing materials at varied length and time scales. X-ray photon correlation spectroscopy (XPCS) is uniquely well-suited for characterizing materials dynamics over wide-ranging time scales. However, spatial and temporal heterogeneity in material behavior can make interpretation of experimental XPCS data difficult. In this work, we have developed an unsupervised deep learning (DL) framework for automated classification of relaxation dynamics from experimental data without requiring any prior physical knowledge of the system. We demonstrate how this method can be used to accelerate exploration of large datasets to identify samples of interest, and we apply this approach to directly correlate microscopic dynamics with macroscopic properties of a model system. Importantly, this DL framework is material and process agnostic, marking a concrete step towards autonomous materials discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A generative modeling approach to reconstructing 21 cm tomographic data

Abstract Analyses of the cosmic 21 cm signal are hampered by astrophysical foregrounds that are far stronger than the signal itself. These foregrounds, typically confined to a wedge-shaped region in Fourier space, often necessitate the removal of a vast majority of modes, thereby degrading the quality of the data anisotropically. To address this challenge, we introduce a novel deep generative model based on stochastic interpolants to reconstruct the 21 cm data lost to wedge filtering. Our method leverages the non-Gaussian nature of the 21 cm signal to effectively map wedge-filtered 3D lightcones to samples from the conditional distribution of wedge-recovered lightcones. We demonstrate how our method is able to restore spatial information effectively, considering both varying cosmological initial conditions and astrophysics. Furthermore, we discuss a number of future avenues where this approach could be applied in analyses of the 21 cm signal, potentially offering new opportunities to improve our understanding of the Universe during the epochs of cosmic dawn and reionization. Code, pre-trained models, and scripts for making plots in this paper can be found here .

Sabti, Nashwan (ORCID:000000027924546X)↗