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

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At least 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Inference of bipolar neutrino flavor oscillations near a core-collapse supernova based on multiple measurements at Earth

Neutrinos in compact-object environments, such as core-collapse supernovae, can experience various kinds of collective effects in flavor space, engendered by neutrino-neutrino interactions. These include “bipolar” collective oscillations, which are exhibited by neutrino ensembles where different flavors dominate at different energies. Considering the importance of neutrinos in the dynamics and nucleosynthesis in these environments, it is desirable to ascertain whether an Earth-based detection could contain signatures of bipolar oscillations that occurred within a supernova envelope. To that end, we, in this study, continue examining a cost-function formulation of statistical data assimilation (SDA) to infer solutions to a small-scale model of neutrino flavor transformation. SDA is an inference paradigm designed to optimize a model with sparse data. Our model consists of two monoenergetic neutrino beams with different energies emanating from a source and coherently interacting with each other and with a matter background, with radially varying interaction strengths. We attempt to infer flavor transformation histories of these beams using simulated measurements of the flavor content at locations “in vacuum” (that is, far from the source), which could in principle correspond to Earth-based detectors. Within the scope of this small-scale model, we found that: (i) based on such measurements, the SDA procedure is able to infer whether bipolar oscillations had occurred within the protoneutron star envelope, and (ii) if the measurements sample the full amplitude of the neutrino oscillations in vacuum, then the amplitude of the prior bipolar oscillations is well predicted. This result intimates that the inference paradigm can well complement numerical integration codes, via its ability to infer flavor evolution at physically inaccessible locations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Implementation of a Binary Neural Network on a Passive Array of Magnetic Tunnel Junctions

The increasing scale of neural networks and their growing application space have produced demand for more energy- and memory-efficient artificial-intelligence-specific hardware. Avenues to mitigate the main issue, the von Neumann bottleneck, include in-memory and near-memory architectures, as well as algorithmic approaches. In this report we leverage the low-power and the inherently binary operation of magnetic tunnel junctions (MTJs) to demonstrate neural network hardware inference based on passive arrays of MTJs. In general, transferring a trained network model to hardware for inference is confronted by degradation in performance due to device-to-device variations, write errors, parasitic resistance, and nonidealities in the substrate. To quantify the effect of these hardware realities, we benchmark 300 unique weight matrix solutions of a two-layer perceptron to classify the Wine dataset for both classification accuracy and write fidelity. Despite device imperfections, we achieve software-equivalent accuracy of up to 95.3% with proper tuning of network parameters in 15 x 15 MTJ arrays having a range of device sizes. The success of this tuning process shows that new metrics are needed to characterize the performance and quality of networks reproduced in mixed signal hardware.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Bayesian differential programming for robust systems identification under uncertainty

This paper presents a machine learning framework for Bayesian systems identification from noisy, sparse and irregular observations of nonlinear dynamical systems. The proposed method takes advantage of recent developments in differentiable programming to propagate gradient information through ordinary differential equation solvers and perform Bayesian inference with respect to unknown model parameters using Hamiltonian Monte Carlo sampling. This allows an efficient inference of the posterior distributions over plausible models with quantified uncertainty, while the use of sparsity-promoting priors enables the discovery of interpretable and parsimonious representations for the underlying latent dynamics. A series of numerical studies is presented to demonstrate the effectiveness of the proposed methods, including nonlinear oscillators, predator–prey systems and examples from systems biology. Taken together, our findings put forth a flexible and robust workflow for data-driven model discovery under uncertainty. All codes and data accompanying this article are available at https://bit.ly/34FOJMj .

Science & Technology - Other Topics↗
DOE OSTI2016

INFERRING THE CORONAL DENSITY IRREGULARITY FROM EUV SPECTRA

Understanding the density structure of the solar corona is important for modeling both coronal heating and the solar wind. Direct measurements are difficult because of line-of-sight integration and possible unresolved structures. We present a new method for quantifying such structures using density-sensitive extreme ultraviolet line intensities to derive a density irregularity parameter, a relative measure of the amount of structure along the line of sight. We also present a simple model to relate the inferred irregularities to physical quantities, such as the filling factor and density contrast. For quiet-Sun regions and interplume regions of coronal holes, we find a density contrast of at least a factor of 3–10 and corresponding filling factors of about 10%–20%. Our results are in rough agreement with other estimates of the density structures in these regions. The irregularity diagnostic provides a useful relative measure of unresolved structure in various regions of the corona.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Computer Model Calibration with Time Series Data Using Deep Learning and Quantile Regression

Computer models play a key role in many scientific and engineering problems. One major source of uncertainty in computer model experiments is input parameter uncertainty. Computer model calibration is a formal statistical procedure to infer input parameters by combining information from model runs and observational data. The existing standard calibration framework suffers from inferential issues when the model output and observational data are high-dimensional dependent data, such as large time series, due to the difficulty in building an emulator and the nonidentifiability between effects from input parameters and data-model discrepancy. To overcome these challenges, we, in this work, propose a new calibration framework based on a deep neural network (DNN) with long short-term memory layers that directly emulates the inverse relationship between the model output and input parameters. Adopting the “learning with noise” idea, we train our DNN model to filter out the effects from data-model discrepancy on input parameter inference. We also formulate a new way to construct interval predictions for DNN using quantile regression to quantify the uncertainty in input parameter estimates. Through a simulation study and real data application with the Weather Research and Forecasting Model Hydrological modeling system (WRF-Hydro), we show our approach can yield accurate point estimates and well-calibrated interval estimates for input parameters.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Uncertainty quantification and propagation in nuclear density functional theory

Nuclear density functional theory (DFT) is one of the main theoretical tools used to study the properties of heavy and superheavy elements, or to describe the structure of nuclei far from stability. While on-going eff orts seek to better root nuclear DFT in the theory of nuclear forces, energy functionals remain semi-phenomenological constructions that depend on a set of parameters adjusted to experimental data in fi nite nuclei. In this study, we review recent eff orts to quantify the related uncertainties, and propagate them to model predictions. In particular, we cover the topics of parameter estimation for inverse problems, statistical analysis of model uncertainties and Bayesian inference methods. Illustrative examples are taken from the literature.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

DeepLensSBI: Deep inference of simulated strong lenses in ground-based surveys

This code is used to train and test machine learning models and generate results and plots presented in 2501.08524 [astro-ph.IM]. The code is written in python. The goal of this work is to train ML models trained on simulated images of strong gravitational lenses. The trained model can then quickly infer properties of the lensed objects with uncertainty quantification.

Poh, Jason [Univ. of Chicago, IL (United States)] ↗
DOE OSTI2024

The Effect of the Prior and the Experimental Design on the Inference of the Precision Matrix in Gaussian Chain Graph Models

Here, we investigate whether (and how) experimental design could aid in the estimation of the precision matrix in a Gaussian chain graph model, especially the interplay between the design, the effect of the experiment and prior knowledge about the effect. Estimation of the precision matrix is a fundamental task to infer biological graphical structures like microbial networks. We compare the marginal posterior precision of the precision matrix under four priors: flat, conjugate Normal-Wishart, Normal-MGIG and a general independent. Under the flat and conjugate priors, the Laplace-approximated posterior precision is not a function of the design matrix rendering useless any efforts to find an optimal experimental design to infer the precision matrix. In contrast, the Normal-MGIG and general independent priors do allow for the search of optimal experimental designs, yet there is a sharp upper bound on the information that can be extracted from a given experiment. We confirm our theoretical findings via a simulation study comparing (i) the KL divergence between prior and posterior and (ii) the Stein’s loss difference of MAPs between random and no experiment. Our findings provide practical advice for domain scientists conducting experiments to better infer the precision matrix as a representation of a biological network.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2010

Modeling Harris Current Sheets with Themis Observations

Current sheets are ubiquitous in nature. occurring in such varied locations as the solar atmosphere. the heliosphere, and the Earth's magnetosphere. The simplest current sheet is the one-dimensional Harris neutral sheet, with the lobe field strength and scale-height the only free parameters. Despite its simplicity, confirmation of the Harris sheet as a reasonable description of the Earth's current sheet has remained elusive. In early 2009 the orbits of the 5 THEMIS probes fortuitously aligned such that profiles of the Earth's current sheet could be modeled in a time dependent manner. For the few hours of alignment we have calculated the time history of the current sheet parameters (scale height and current) in the near-Earth region. during both quiet and active times. For one particular substorm. we further demonstrate good quantitative agreement with the diversion of cross tail current inferred from the Harris modeling with the ionospheric current inferred from ground magnetometer data.

Kepko, L.↗
NASA NTRS1995

Variability of moisture in the upper troposphere as inferred from TOVS satellite observations and the ECMWF model analyses in 1989

Large-scale variability of moisture in the upper troposphere is examined using TIROS (Television Infrared Observation Satellite) Operational Vertical Sounder (TOVS) satellite observations and European Centre for Medium-Range Weather Forecasts (ECMWF) model analyses from 1989 in the latitude band from 40 deg N to 40 S. To compare these dissimilar datasets, upwelling radiances were computed for the 6-7 micrometer water vapor band from the ECMWF temperature and moisture analyses, and these computed radiances were compared to the corresponding TOVS satellite observations. The ECMWF-based radiances reproduce the general locations and seasonal cycle of the TOVS-observed moisture features, particularly after an improved convective parameterization scheme was adopted by ECMWF in May 1989. However, the ECMWF analysis scheme still results in much milder lateral moisture gradients and seasonal contrasts than indicated by the TOVS observations. Seasonally, the upper troposphere in each hemisphere dries in winter and moistens in summer, but there are regions in each hemisphere that run counter to this seasonal trend, apparently depending on continental- and monsoon-scale dynamics. Dynamically, the TOVS-observed regions of significant subtropical dryness are correlated with persistent subsidence indicated by ECMWF 300-mb vertical velocity analyses. The TOVS radiance observations indicate large variations in space and time of the upper-tropospheric moisture field, which are not fully captured by the ECMWF analyses.

Salathe, Eric P., Jr.↗
NASA NTRS2022

Vertical Structure in Phytoplankton Growth and Productivity Inferred From Biogeochemical-Argo Floats and the Carbon-Based Productivity Model

Estimates of marine net primary production (NPP) commonly rely on limited in situ 14C incubations or satellite-based algorithms mainly constrained to the surface ocean. Here we combine data from biogeochemical Argo floats with a carbon-based productivity model (CbPM) to compute vertically-resolved estimates of NPP. Inferred NPP profiles derived by informing the CbPM with float-based, depth-resolved, bio-optical data are able to qualitatively reproduce the vertical structure in NPP inferred from in situ 14C incubations at various ocean regions. At station ALOHA, float-based estimates agree within uncertainty with productivity observations at depth, but underestimate surface NPP. We test the ability of the CbPM to infer the depth-resolved structure in NPP from bio-optical properties in the mixed layer, in similar fashion as how remote sensing algorithms of ocean productivity operate. In Southern Ocean waters, the depth-reconstructing implementation of the CbPM overestimates phytoplankton division rates and Chl:C below the mixed layer, resulting in artificially high subsurface NPP when compared with the fully float-informed implementation of the model. The CbPM subsurface extrapolation of phytoplankton Chl, Chl:C division rates, and NPP improves by accounting for deep nutrient (iron) stress impacts on photoacclimation in the Southern Ocean. This improvement is also observed in vertically- integrated NPP, where the mean bias between model implementations in depth-integrated productivity south of 30◦S is reduced by 62 % when account for deep iron limitation. Our results demonstrate that profiling data from biogeochemical Argo floats can serve to inform regional adjustments that lead to the improvement of marine productivity algorithms.

NPP↗
NASA NTRS1994

How well was total ozone abundance inferred with Mariner 9?

Ozone is a key to understanding atmospheric chemistry on Mars. Over 20 photochemical models of the martian atmosphere have been published, and O3 is often used as a benchmark for these models. O3 abundance has been inferred from instrumentation on several spacecraft, with the most complete coverage provided by Mariner 9. The Mariner 9 UV spectrometer scanned from 2100 to 3500 Angstroms with a spectral resolution of 15 Angstroms and an effective field-of-view of approximately 300 sq km. The only atmospheric absorption in the 2000 to 3000 A wavelength region was previously assumed to come from the Hartley band system of ozone, which has an opacity of order unity at winter polar latitudes. Therefore, the amount of ozone was inferred by fitting this absorption feature with laboratory data of ozone absorption. Mars O3 shows strong seasonal and latitudinal variation, with column abundances ranging from 0.2 micron-atm at equatorial latitudes to 60 micron-atm over northern winter polar latitudes (1 micron-atm is a column abundance of 2.689 x 10 exp 15 molecules cm(exp -2)). However, the O3 abundance is never great enough to significantly affect atmospheric temperatures or surface temperatures and frost amounts. Some of the previously-inferred O3 abundances are shown. A radiative transfer computer model is used to re-examine the Mariner 9 UV spectra. Assuming a constant mixing ratio for O3 and no chemical or radiative interaction between O3 and clouds/dust, it is shown that when typical amounts of dust and cloud are present that significant underestimation of O3 abundance occurs. A factor of 3 times as much O3 is needed to generate the same spectrum the spacecraft would measure for a cloudy, dusty atmosphere as for a clear atmosphere. If the scattering properties of martian clouds and dust were well known, then their appearance would not be a problem, as a model would be capable of retrieving the O3 abundance. However, these properties are not well known, which raises doubts about the effectiveness of the UV reflectance spectroscopy technique for measuring O3 abundance on Mars.

Lindner, Bernhard Lee↗