Tropical Pacific and Indian-Ocean warming during 1997-98 inferred by assimilating TOPEX/Poseidon data into near-global OGCM
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TOPEX/Poseidon OGCM Pacific Ocean Indian Ocean oce↗
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Results for “data inference”
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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Accurate geometrical inferences from proton radiography data using the CpRad forward model
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Tang, Elise↗
Architectural Implications of Neural Network Inference for High Data-Rate, Low-Latency Scientific Applications
With more scientific fields relying on neural networks (NNs) to process data incoming at extreme throughputs and latencies, it is crucial to develop NNs with all their parameters stored on-chip. In many of these applications, there is not enough time to go off-chip and retrieve weights. Even more so, off-chip memory such as DRAM does not have the bandwidth required to process these NNs as fast as the data is being produced (e.g., every 25 ns). As such, these extreme latency and bandwidth requirements have architectural implications for the hardware intended to run these NNs: 1) all NN parameters must fit on-chip, and 2) codesigning custom/reconfigurable logic is often required to meet these latency and bandwidth constraints. In our work, we show that many scientific NN applications must run fully on chip, in the extreme case requiring a custom chip to meet such stringent constraints.
Weng, Olivia↗
Monitoring Operational States of a Nuclear Reactor Using Seismoacoustic Signatures and Machine Learning
Monitoring nuclear reactors is an important safety and security task with growing requirements. We explore the possibility of using seismic and acoustic data for inferring the power level of an operating reactor. Continuous data recorded at a single seismoacoustic station that is located about 50 m away from a research reactor was visualized and analyzed. The data show a clear correlation between seismoacoustic features and reactor main operational states. We designed a workflow that includes two machine learning (ML) models to classify the reactor operational states (OFF, transition, and ON) and estimate reactor power levels (10%, 30%, 50%, 70%, and 90%). We applied and compared five ML algorithms for the reactor OFF-transition-ON and four approaches for the power level classification. We also compared the performance of ML models trained with seismic-only, acoustic-only, and both types of data. Five-fold cross validations were implemented to assure a thorough evaluation of the model performances. Additionally, the results show the extreme boosting gradient algorithm worked best for the first model, whereas random forests performed best for the second model. Combining seismic and acoustic data leads to better performance than using a single type of data. Seismic data contributed more than acoustic data for both models. We reached an accuracy of 0.98 for reactor OFF and ON. The accuracies for the transition state and power levels are less optimal with a minimum accuracy of 0.66. However, our results suggest seismic and acoustic data contain useful information about the transition state as well as power levels. Seismic and acoustic data could be integrated with other observations to improve monitoring performance.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Privacy policy robustness to reverse engineering
Differential privacy policies allow one to preserve data privacy while sharing and analyzing data. However, these policies are susceptible to an array of attacks. In particular, often a portion of the data desired to be privacy protected is exposed online. Access to these pre-privacy protected data samples can then be used to reverse engineer the privacy policy. With knowledge of the generating privacy policy, an attacker can use machine learning to approximate the full set of originating data. Bayesian inference is one method for reverse engineering both model and model parameters. We present a methodology for evaluating and ranking privacy policy robustness to Bayesian inference-based reverse engineering, and demonstrated this method across data with a variety of temporal trends.
Kusne, Aaron Gilad↗
Case studies using GOES infrared data and a planetary boundary layer model to infer regional scale variations in soil moisture
Modeled temperature data from a one-dimensional, time-dependent, initial value, planetary boundary layer model for 16 separate model runs with varying initial values of moisture availability are applied, by the use of a regression equation, to longwave infrared GOES satellite data to infer moisture availability over a regional area in the central U.S. This was done for several days during the summers of 1978 and 1980 where a large gradient in the antecedent precipitation index (API) represented the boundary between a drought area and a region of near normal precipitation. Correlations between satellite derived moisture availability and API were found to exist. Errors from the presence of clouds, water vapor and other spatial inhomogeneities made the use of the measurement for anything except the relative degree of moisture availability dubious.
Rose, F. G.↗
Physics-Informed Gaussian Process Inference of Liquid Structure from Scattering Data
We present a nonparametric Bayesian framework to infer radial distribution functions from experimental scattering measurements with uncertainty quantification using nonstationary Gaussian processes. The Gaussian process prior mean and kernel functions are designed to mitigate well-known numerical challenges with the Fourier transform, including discrete measurement binning and detector windowing, while encoding fundamental yet minimal physical knowledge of the liquid structure. We demonstrate uncertainty propagation of the Gaussian process posterior to unmeasured quantities of interest. Experimental radial distribution functions of liquid argon and water with uncertainty quantification are provided as both a proof of principle for the method and a benchmark for molecular models.
Chemical structure↗
Data Assimilation of Photosynthetic Light-use Efficiency using Multi-angular Satellite Data: II Model Implementation and Validation
Spatially explicit and temporally continuous estimates of photosynthesis will be of great importance for increasing our understanding of and ultimately closing the terrestrial carbon cycle. Current capabilities to model photosynthesis, however, are limited by accurate enough representations of the complexity of the underlying biochemical processes and the numerous environmental constraints imposed upon plant primary production. A potentially powerful alternative to model photosynthesis through these indirect observations is the use of multi-angular satellite data to infer light-use efficiency (ε) directly from spectral reflectance properties in connection with canopy shadow fractions. Hall et al. (this issue) introduced a new approach for predicting gross ecosystem production that would allow the use of such observations in a data assimilation mode to obtain spatially explicit variations in ε from infrequent polar-orbiting satellite observations, while meteorological data are used to account for the more dynamic responses of ε to variations in environmental conditions caused by changes in weather and illumination. In this second part of the study we implement and validate the approach of Hall et al. (this issue) across an ecologically diverse array of eight flux-tower sites in North America using data acquired from the Compact High Resolution Imaging Spectroradiometer (CHRIS) and eddy-flux observations. Our results show significantly enhanced estimates of ε and therefore cumulative gross ecosystem production (GEP) over the course of one year at all examined sites. We also demonstrate that ε is greatly heterogeneous even across small study areas. Data assimilation and direct inference of GEP from space using a new, proposed sensor could therefore be a significant step towards closing the terrestrial carbon cycle.
Hilker, Thomas↗
An interdisciplinary analysis of Colorado Rocky Mountain environments using ADP techniques
The author has identified the following significant results. Good ecological, classification accuracy (90-95%) can be achieved in areas of rugged relief on a regional basis for Level 1 cover types (coniferous forest, deciduous forest, grassland, cropland, bare rock and soil, and water) using computer-aided analysis techniques on ERTS/MSS data. Cost comparisons showed that a Level 1 cover type map and a table of areal estimates could be obtained for the 443,000 hectare San Juan Mt. test site for less than 0.1 cent per acre, whereas photointerpretation techniques would cost more than 0.4 cent per acre. Results of snow cover mapping have conclusively proven that the areal extent of snow in mountainous terrain can be rapidly and economically mapped by using ERTS/MSS data and computer-aided analysis techniques. A distinct relationship between elevation and time of freeze or thaw was observed, during mountain lake mapping. Basic lithologic units such as igneous, sedimentary, and unconsolidated rock materials were successfully identified. Geomorphic form, which is exhibited through spatial and textual data, can only be inferred from ERTS data. Data collection platform systems can be utilized to produce satisfactory data from extremely inaccessible locations that encounter very adverse weather conditions, as indicated by results obtained from a DCP located at 3,536 meters elevation that encountered minimum temperatures of -25.5 C and wind speeds of up to 40.9m/sec (91 mph), but which still performed very reliably.
Hoffer, R. M.↗
LACIE applications evaluation system efficiency report
The scope of the three LACIE phases is discussed as well as the system efficiencies which had to be implemented to cope with the resulting LANDSAT data load. The methodologies used in system analysis, some of the specific data collected, and the inferences of these data and their implication on future systems are also discussed.
White, T. T.↗
Trends in Antarctic Surface Temperatures and Ice Extent from Satellite and Surface Observations
Surface air temperatures observed from stations around Antarctica have indicated predominantly positive trends which are as high as 0.4K per decade along the Antarctic Peninsula. Since the average air temperature during summer in the West Antarctic region is about -6 K, and since most of the stations are near coastal regions, it is important to know whether such trends are due to a local or large scale phenomenon. We analyzed about 19 years of infrared satellite data (1979-1997) to obtain a spatially detailed study of the variability of surface temperatures over the entire Antarctic region. We also analyzed potential impact of temperature changes over the sea ice cover using co-registered and nearly coincident microwave satellite data. The surface temperatures inferred from infrared data are shown to agree well with those observed from the limited number of Antarctic station data with a correlation coefficient of 0.98 and a standard deviation of less than 3K. The 19-year temperature data set shows positive trends of 0.12, 0.68, and 0.41 C per decade in ocean, sea ice and ice sheet surfaces, respectively. The sensitivity of the trend to record length was studied, using long term station data sets, and the results show that the trends start to stabilize at around 2 decades which is about the record length of satellite data. However, interannual fluctuations of the temperatures are large, especially in the ice sheets, and the 95% confidence level for the trends ranges from 0.12 to 1.22 for sea ice and from -0.74 to 1.55 C per decade for the ice sheet. In the sea ice regions, the position of the ice edge in winter with respect to the northernmost position of sea ice during the study period is also shown to be influenced by alternating warm and cold anomalies around the continent. This pattern is similar to that suggested by the presence of the Antarctic Circumpolar Wave (ACW) but the wavenumber appears to be predominantly mode 3 (instead of reported mode 2) for the Antarctic region. Unexpectedly, trends in the sea ice extent are found to be positive in most Antarctic sectors during the same period with the exception of the Bellingshausen and Amundsen Seas region.
Comisc, J. C.↗
Topography of Closed Depressions, Scarps, and Grabens in the North Tharsis Region of Mars: Implications for Shallow Crustal Discontinuities and Graben Formation
Using Viking Orbiter images, detailed photoclinometric profiles were obtained across irregular depressions, fretted fractures, troughs and pits, solitary scarps, and simple grabens in the north Tharsis region of Mars. This data allows inferences to be made on the shallow crustal structure of this region. Topographical data obtained for Mars is discussed in relation to mechanical discontinuities.
astrogeology↗
Better practices for inferring ecosystem water use strategy from eddy covariance data
Eddy covariance data are critical for inferring ecosystem water use strategies. Yet, such inferences are sensitive to a range of assumptions applied across studies, hindering our understanding of water use strategies within and across eddy covariance sites. A recent analysis across 151 FLUXNET2015 and AmeriFlux-FLUXNET datasets found that poor model performance was the key driver of non-robust inferences of ecosystem water use strategies. Here, we leverage this previous analysis to (i) identify the specific assumptions that improve inference model performance across most sites, (ii) explain the mechanisms behind the performance improvements, and (iii) check whether better performance improves water use inference. We find that the common practice of fitting a model to canopy conductance (G c ) derived from the evapotranspiration (ET) observations, rather than to observed ET itself, artificially amplifies data errors and degrades the model performance. Next, accounting for vegetation dynamics by applying a growing season filter or incorporating satellite LAI data improves performance, but the former practice may remove soil water stress periods. Lastly, using the leaf-to-air vapor pressure deficit (VPD l ) derived from ET observations as a model input may artificially inflate performance. Based on these results, we recommend selecting observed ET (rather than derived G c ) as the response variable, carefully accounting for vegetation dynamics, and avoiding derived VPD l as a model input; these best practices improve model performance by c. 20% and robustness by c. 80% across all eddy covariance sites. Nevertheless, the performance improvements do not always correspond to more robust inference of water use strategies, as model parameter selection and surface energy budget closure corrections still strongly influence the ecosystem water use parameter estimation in a site-specific manner.
AmeriFlux↗
Ripening of Rh Nanoparticle Catalysts in Reverse Water–Gas Shift via a Data-Driven Model Combining Physics, Theory, and Experiment
Degradation via sintering is an ongoing challenge that impedes the broad commercial success of supported metallic nanoparticle catalysts. To mitigate degradation via informed catalyst design and process operations, here we aim to disambiguate the underlying mechanisms of sintering by combining theory and experiment in a quantitative framework. While mechanistic sintering models exist, they only model a single sintering pathway, even though multiple sintering mechanisms can occur simultaneously or dominate at different stages of the process. Data-driven machine learning models have emerged as a means to represent complex processes through data regression. However, machine learning models have very large data needs and lack mechanistic insights due to their black-box encoding. To develop an interpretive model of catalyst degradation via sintering, we constructed a hybrid model combining mechanistic “physics-based” models and data-driven methods to obtain both reliable predictions and mechanistic insights regarding experimentally observed sintering phenomena. Focusing on nanoparticle sintering in the Rh–TiO 2 catalyst for the reverse water–gas shift (RWGS) reaction, the hybrid model couples a mechanistic term for Ostwald ripening with energy values calculated via density functional theory (DFT) with a parametric, data-driven discrepancy function term for unmodeled mechanisms. The hybrid model is trained using Bayesian inference with data collected from small-angle X-ray scattering (SAXS) in situ experiments wherein average nanoparticle diameter versus time was measured at three relevant operating temperatures. The calibrated hybrid model results show that an Ostwald ripening-only model parameterized with fixed DFT energies does not fully capture the time and temperature dependence of the SAXS-observed sintering kinetics, and that an additional functional contribution, or DFT energy calibration, is required to reconcile simulation and experiment. Analysis of the hybrid-model error confirms that the hybrid model outperforms both the purely mechanistic and purely data-driven alternatives in terms of expected predictive accuracy for time-evolving average particle sizes. Furthermore, the results support the hypothesis that the Ostwald ripening mechanism is less important for explaining the sintering phenomena as operating temperature increases under an assumed fixed DFT parameterization. This could be explained in one of two ways: either latent, unmodeled sintering mechanisms dominate at higher temperatures, or the DFT uncertainty increases with temperature. The proposed modeling approach directly links theory to experiments and simulations via a statistical hybrid modeling framework and can be extended to other catalytic systems to improve predictive models and mechanistic understanding.
Bayesian hybrid modeling↗
Super-Resolution Approaches in Three-Dimensions for Classification and Screening of Commercial-Off-The-Shelf Components
X-ray computed tomography is generally a primary step in characterization of defective electronic components, but is generally too slow to screen large lots of components. Super-resolution imaging approaches, in which higher-resolution data is inferred from lower-resolution images, have the potential to substantially reduce collection times for data volumes accessible via x-ray computed tomography. Here we seek to advance existing two-dimensional super-resolution approaches directly to three-dimensional computed tomography data. Multiple scan resolutions over a half order of magnitude of resolution were collected for four classes of commercial electronic components to serve as training data for a deep-learning, super-resolution network. A modular python framework for three-dimensional super-resolution of computed tomography data has been developed and trained over multiple classes of electronic components. Initial training and testing demonstrate the vast promise for these approaches, which have the potential for more than an order of magnitude reduction in collection time for electronic component screening.
47 OTHER INSTRUMENTATION↗
Characterization of the pressure coefficient of manganin and temperature evolution of pressure in piston-cylinder cells
Here, we report measurements of the temperature- and pressure-dependent resistance, R ( T , p ), of a manganin manometer in a 4 He-gas pressure setup from room temperature down to the solidification temperature of 4 He ( T solid ~ 50 K at 0.8 GPa) for pressures, p , between 0 GPa and ~0.8 GPa. The same manganin wire manometer was also measured in a piston-cylinder cell (PCC) from 300 K down to 1.8 K and for pressures between 0 GPa and ~2 GPa. From these data, we infer the temperature and pressure dependence of the pressure coefficient of manganin, α ( T , p ), defined by the equation R p = (1 + αp ) R 0 , where R 0 and R p are the resistances of manganin at ambient pressure and finite pressure, respectively. Our results indicate that upon cooling, α first decreases, then goes through a broad minimum at ~120 K, and increases again toward lower temperatures. In addition, we find that α is almost pressure-independent at T ≳ 60 K up to p ~ 2 GPa, but shows a pronounced p dependence at T ≲ 60 K. Using this manganin manometer, we demonstrate that p overall decreases with decreasing temperature in the PCC for the full pressure range and that the size of the pressure difference between room temperature and low temperatures ( T = 1.8 K), Δ p , decreases with increasing pressure. We also compare the pressure values inferred from the manganin manometer with the low-temperature pressure, determined from the superconducting transition temperature of elemental lead (Pb). As a result of these data and analysis, we propose a practical algorithm to infer the evolution of pressure with temperature in a PCC.
47 OTHER INSTRUMENTATION↗
Strong Lensing Parameter Estimation on Ground-Based Imaging Data Using Simulation-Based Inference
Current ground-based cosmological surveys, such as the Dark Energy Survey (DES), are predicted to discover thousands of galaxy-scale strong lenses, while future surveys, such as the Vera Rubin Observatory Legacy Survey of Space and Time (LSST) will increase that number by 1-2 orders of magnitude. The large number of strong lenses discoverable in future surveys will make strong lensing a highly competitive and complementary cosmic probe. To leverage the increased statistical power of the lenses that will be discovered through upcoming surveys, automated lens analysis techniques are necessary. We present two Simulation-Based Inference (SBI) approaches for lens parameter estimation of galaxy-galaxy lenses. We demonstrate the successful application of Neural Posterior Estimation (NPE) to automate the inference of a 12-parameter lens mass model for DES-like ground-based imaging data. We compare our NPE constraints to a Bayesian Neural Network (BNN) and find that it outperforms the BNN, producing posterior distributions that are for the most part both more accurate and more precise; in particular, several source-light model parameters are systematically biased in the BNN implementation.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗