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At least 1,045 records · Page 58

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DOE OSTI2023

Canonical and Noncanonical Hamiltonian Operator Inference

SAND2024-08577O Canonical and Noncanonical Hamiltonian Operator Inference is research code that applies old and new model reduction algorithms to well-known and publicly available "toy" problems. It applies operator inference (OpInf) methods for model reduction to several benchmark hyperbolic partial differential equations: linear wave, Korteweg-de Vries, Benjamin-Bona-Mahoney, and linear elasticity. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

SciDAC↗

DOE OSTI2021

Structurally flexible cloud microphysics, observationally constrained at all scales via ML-accelerated Bayesian inference

We discuss the challenge of developing observationally informed parameterizations of microphysics for use at a hierarchy of modeling scales. Our proposed approach is applicable to any domain that suffers from a two-fold parameterization problem, where physical processes are not resolved at the model scale (the first problem), and those processes are uncertain at any scale (the second problem). For such problems, a physical approach facilitates modeling across scales, as well as systematic observational inference accelerated by machine learning (ML) surrogate models, and quantification of physical uncertainties.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

Theory and observations of upward field‐aligned currents at the magnetopause boundary layer

Abstract The dependence of the upward field‐aligned current density ( J || ) at the dayside magnetopause boundary layer is well described by a simple analytic model based on a velocity shear generator. A previous observational survey confirmed that the scaling properties predicted by the analytical model are applicable between 11 and 17 MLT. We utilize the analytic model to predict field‐aligned currents using solar wind and ionospheric parameters and compare with direct observations. The calculated and observed parallel currents are in excellent agreement, suggesting that the model may be useful to infer boundary layer structures. However, near noon, where velocity shear is small, the kinetic pressure gradients and thermal currents, which are not included in the model, could make a small but significant contribution to J || . Excluding data from noon, our least squares fit returns log( J ||,max_cal ) = (0.96 ± 0.04) log( J ||_obs ) + (0.03 ± 0.01) where J ||,max_cal = calculated J ||,max and J ||_obs = observed J || .

Wing, Simon↗

DOE OSTI2025

The Importance of Being Adaptable: An Exploration of the Power and Limitations of Domain Adaptation for Simulation-Based Inference with Galaxy Clusters

The application of deep machine learning methods in astronomy has exploded in the last decade, with new models showing remarkably improved performance on benchmark tasks. Not nearly enough attention is given to understanding the models' robustness, especially when the test data are systematically different from the training data, or "out of domain." Domain shift poses a significant challenge for simulation-based inference, where models are trained on simulated data but applied to real observational data. In this paper, we explore domain shift and test domain adaptation methods for a specific scientific case: simulation-based inference for estimating galaxy cluster masses from X-ray profiles. We build datasets to mimic simulation-based inference: a training set from the Magneticum simulation, a scatter-augmented training set to capture uncertainties in scaling relations, and a test set derived from the IllustrisTNG simulation. We demonstrate that the Test Set is out of domain in subtle ways that would be difficult to detect without careful analysis. We apply three deep learning methods: a standard neural network (NN), a neural network trained on the scatter-augmented input catalogs, and a Deep Reconstruction-Regression Network (DRRN), a semi-supervised deep model engineered to address domain shift. Although the NN improves results by 17% in the Training Data, it performs 40% worse on the out-of-domain Test Set. Surprisingly, the Scatter-Augmented Neural Network (SANN) performs similarly. While the DRRN is successful in mapping the training and Test Data onto the same latent space, it consistently underperforms compared to a straightforward Yx scaling relation. These results serve as a warning that simulation-based inference must be handled with extreme care, as subtle differences between training simulations and observational data can lead to unforeseen biases creeping into the results.

Ntampaka, Michelle [Baltimore, Space Telescope Sci↗

DOE OSTI2017

A Bayesian approach to modeling diffraction profiles and application to ferroelectric materials

A new statistical approach for modeling diffraction profiles is introduced, using Bayesian inference and a Markov chain Monte Carlo (MCMC) algorithm. This method is demonstrated by modeling the degenerate reflections during application of an electric field to two different ferroelectric materials: thin-film lead zirconate titanate (PZT) of composition PbZr 0.3 Ti 0.7 O 3 and a bulk commercial PZT polycrystalline ferroelectric. Here, the new method offers a unique uncertainty quantification of the model parameters that can be readily propagated into new calculated parameters.

36 MATERIALS SCIENCE↗

NASA NTRS2013

Inferring Soil Moisture Memory from Streamflow Observations Using a Simple Water Balance Model

Soil moisture is known for its integrative behavior and resulting memory characteristics. Soil moisture anomalies can persist for weeks or even months into the future, making initial soil moisture a potentially important contributor to skill in weather forecasting. A major difficulty when investigating soil moisture and its memory using observations is the sparse availability of long-term measurements and their limited spatial representativeness. In contrast, there is an abundance of long-term streamflow measurements for catchments of various sizes across the world. We investigate in this study whether such streamflow measurements can be used to infer and characterize soil moisture memory in respective catchments. Our approach uses a simple water balance model in which evapotranspiration and runoff ratios are expressed as simple functions of soil moisture; optimized functions for the model are determined using streamflow observations, and the optimized model in turn provides information on soil moisture memory on the catchment scale. The validity of the approach is demonstrated with data from three heavily monitored catchments. The approach is then applied to streamflow data in several small catchments across Switzerland to obtain a spatially distributed description of soil moisture memory and to show how memory varies, for example, with altitude and topography.

Soil Moisture↗

NASA NTRS1999

Modeling the QBO and SAO Driven by Gravity Waves

Hines' Doppler spread parameterization (DSP) for small scale gravity waves (GW) is applied in a global scale numerical spectral model (NSM) to describe the semi-annual and quasi-biennial oscillations (SAO and QBO) as well as the long term interannual variations that are driven by wave mean flow interactions. This model has been successful in simulating the salient features observed near the equator at altitudes above 20 km, including the QBO extension into the upper mesosphere inferred from UARS measurements. The model has now been extended to describe also the mean zonal and meridional circulations of the upper troposphere and lower stratosphere that affect the equatorial QBO and its global scale extension. This is accomplished in part through tuning of the GW parameterization, and preliminary results lead to the following conclusions: (1) To reproduce the upwelling at equatorial latitudes associated with the Brewer/Dobson circulation that in part is modulated in the model by the vertical component of the Coriolis force, the eddy diffusivity in the lower stratosphere had to be enhanced and the related GW spectrum modified to bring it in closer agreement with the form recommended for the DSP. (2) To compensate for the required increase in the diffusivity, the observed QBO requires a larger GW source that is closer to the middle of the range recommended for the DSP. (3) Through global scale momentum redistribution, the above developments are conducive to extending the QBO and SAO oscillations to higher latitudes. Multi-year interannual oscillations are generated through wave filtering by the solar driven annual oscillation in the zonal circulation. (4) In a 3D version of the model, wave momentum is absorbed and dissipated by tides and planetary waves. Thus, a somewhat larger GW source is required to generate realistic amplitudes for the QBO and SAO.

Mayr, H. G.↗

NASA NTRS1983

Ionospheric and Birkeland current distributions inferred from the MAGSAT magnetometer data

Ionospheric and field-aligned sheet current density distributions are presently inferred by means of MAGSAT vector magnetometer data, together with an accurate magnetic field model. By comparing Hall current densities inferred from the MAGSAT data and those inferred from simultaneously recorded ground based data acquired by the Scandinavian magnetometer array, it is determined that the former have previously been underestimated due to high damping of magnetic variations with high spatial wave numbers between the ionosphere and the MAGSAT orbit. Among important results of this study is noted the fact that the Birkeland and electrojet current systems are colocated. The analyses have shown a tendency for triangular rather than constant electrojet current distributions as a function of latitude, consistent with the statistical, uniform regions 1 and 2 Birkeland current patterns.

Zanetti, L. J.↗

NASA NTRS1988

Tracer water transport and subgrid precipitation variation within atmospheric general circulation models

A capability is developed for monitoring tracer water movement in the three-dimensional Goddard Institute for Space Science Atmospheric General Circulation Model (GCM). A typical experiment with the tracer water model follows water evaporating from selected grid squares and determines where this water first returns to the Earth's surface as precipitation or condensate, thereby providing information on the lateral scales of hydrological transport in the GCM. Through a comparison of model results with observations in nature, inferences can be drawn concerning real world water transport. Tests of the tracer water model include a comparison of simulated and observed vertically-integrated vapor flux fields and simulations of atomic tritium transport from the stratosphere to the oceans. The inter-annual variability of the tracer water model results is also examined.

Koster, Randal D.↗

DOE OSTI2018

Measuring the Viewing Angle of GW170817 with Electromagnetic and Gravitational Waves

The joint detection of gravitational waves (GWs) and electromagnetic (EM) radiation from the binary neutron star merger GW170817 ushered in a new era of multi-messenger astronomy. Joint GW–EM observations can be used to measure the parameters of the binary with better precision than either observation alone. Here, we use joint GW–EM observations to measure the viewing angle of GW170817, the angle between the binary's angular momentum and the line of sight. We combine a direct measurement of the distance to the host galaxy of GW170817 (NGC 4993) of 40.7 ± 2.36 Mpc with the Laser Interferometer Gravitational-wave Observatory (LIGO)/Virgo GW data and find that the viewing angle is ${32}_{-13}^{+10}\,\pm 1.7$ degrees (90% confidence, statistical, and systematic errors). We place a conservative lower limit on the viewing angle of ≥13°, which is robust to the choice of prior. This measurement provides a constraint on models of the prompt γ-ray and radio/X-ray afterglow emission associated with the merger; for example, it is consistent with the off-axis viewing angle inferred for a structured jet model. Lastly, we provide for the first time the full posterior samples from Bayesian parameter estimation of LIGO/Virgo data to enable further analysis by the community.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1981

EUV flux variations during solar cycle 21 from AE-E He/+/ abundances

The ionization rate of helium in the thermosphere is inferred from AE-E ion and neutral particle composition measurements, made on several days during solar cycle 21, in the context of a steady state photochemical model for helium ions. The inferred ionization rate varies by a factor of 4 between July 1976 and January 1979, a value consistent with the variation in the flux measured during the same period by the EUV spectrometer on board the AE-E satellite.

Oppenheimer, M.↗

NASA NTRS2000

NGC 1614: A Laboratory for Starburst Evolution

The modest extinction and reasonably face-on viewing geometry make the luminous infrared galaxy NGC 1614 an ideal laboratory for study of a powerful starburst. HST/NICMOS observations show: (1) deep CO stellar absorption, tracing a starburst nucleus about 45 pc in diameter; (2) surrounded by an approx. 600 pc diameter ring of supergiant H II regions revealed in Pa-alpha line emission; (3) lying within a molecular ring indicated by its extinction shadow in H - K; and (4) all at the center of a disturbed spiral galaxy. The luminosities of the giant H II regions in the ring axe extremely high, an order of magnitude brighter than 30 Doradus; very luminous H II regions, comparable with 30 Dor, are also found in the spiral arms of the galaxy. Luminous stellar clusters surround the nucleus and lie in the spiral arms, similar to clusters observed in other infrared luminous and ultraluminous galaxies. The star forming activity may have been initiated by a merger between a disk galaxy and a companion satellite, whose nucleus appears in projection about 300 pc to the NE of the nucleus of the primary galaxy. The relation of deep stellar CO bands to surrounding ionized gas ring to molecular gas indicates that the luminous starburst started in the nucleus and is propagating outward into the surrounding molecular ring. This hypothesis is supported by evolutionary starburst modeling that shows that the properties of NGC 1614 can be fitted with two short-lived bursts of star formation separated by 5 Myr (and by inference by a variety of models with a similar duration of star formation). The total dynamical mass of the starburst region of 1.3 x 10(exp 9) solar masses is mostly accounted for by the old pre-starburst stellar population. Although our starburst models use a modified Salpeter initial mass function (turning over near one solar mass), the tight mass budget suggests that the IMF may contain relatively more 10 - 30 solar masses stars and fewer low mass stars than the Salpeter function. The dynamical mass is nearly four times smaller than the mass of molecular gas estimated from the standard ratio of (C-12)O (1 - 0) to H2. A number of arguments place the mass of gas in the starburst region at approx. 25% of the dynamical mass, nominally about 1/15 and with an upper limit of 1/10 of the amount estimated from (C-12)O and the standard ratio.

Alonso-Herrero, A.↗

NASA NTRS1998

Model Based Autonomy for Robust Mars Operations

Space missions have historically relied upon a large ground staff, numbering in the hundreds for complex missions, to maintain routine operations. When an anomaly occurs, this small army of engineers attempts to identify and work around the problem. A piloted Mars mission, with its multiyear duration, cost pressures, half-hour communication delays and two-week blackouts cannot be closely controlled by a battalion of engineers on Earth. Flight crew involvement in routine system operations must also be minimized to maximize science return. It also may be unrealistic to require the crew have the expertise in each mission subsystem needed to diagnose a system failure and effect a timely repair, as engineers did for Apollo 13. Enter model-based autonomy, which allows complex systems to autonomously maintain operation despite failures or anomalous conditions, contributing to safe, robust, and minimally supervised operation of spacecraft, life support, In Situ Resource Utilization (ISRU) and power systems. Autonomous reasoning is central to the approach. A reasoning algorithm uses a logical or mathematical model of a system to infer how to operate the system, diagnose failures and generate appropriate behavior to repair or reconfigure the system in response. The 'plug and play' nature of the models enables low cost development of autonomy for multiple platforms. Declarative, reusable models capture relevant aspects of the behavior of simple devices (e.g. valves or thrusters). Reasoning algorithms combine device models to create a model of the system-wide interactions and behavior of a complex, unique artifact such as a spacecraft. Rather than requiring engineers to all possible interactions and failures at design time or perform analysis during the mission, the reasoning engine generates the appropriate response to the current situation, taking into account its system-wide knowledge, the current state, and even sensor failures or unexpected behavior.

Kurien, James A.↗

DOE OSTI2017

Estimating the Contribution of Dynamical Ejecta in the Kilonova Associated with GW170817

The source of the gravitational-wave (GW) signal GW170817, very likely a binary neutron star merger, was also observed electromagnetically, providing the first multi-messenger observations of this type. The two-week-long electromagnetic (EM) counterpart had a signature indicative of an r-process-induced optical transient known as a kilonova. This Letter examines how the mass of the dynamical ejecta can be estimated without a direct electromagnetic observation of the kilonova, using GW measurements and a phenomenological model calibrated to numerical simulations of mergers with dynamical ejecta. Specifically, we apply the model to the binary masses inferred from the GW measurements, and use the resulting mass of the dynamical ejecta to estimate its contribution (without the effects of wind ejecta) to the corresponding kilonova light curves from various models. The distributions of dynamical ejecta mass range between ${M}_{\mathrm{ej}}={10}^{-3}-{10}^{-2}\,{M}_{\odot }$ for various equations of state, assuming that the neutron stars are rotating slowly. In addition, we use our estimates of the dynamical ejecta mass and the neutron star merger rates inferred from GW170817 to constrain the contribution of events like this to the r-process element abundance in the Galaxy when ejecta mass from post-merger winds is neglected. We find that if ≳10% of the matter dynamically ejected from binary neutron star (BNS) mergers is converted to r-process elements, GW170817-like BNS mergers could fully account for the amount of r-process material observed in the Milky Way.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Bayesian inference for the seismic moment tensor using regional waveforms and a data-derived distribution of velocity models

The largest source of uncertainty in any source inversion is the velocity model used to construct the transfer function employed in the forward model that relates observed ground motion to the seismic moment tensor. However, standard inverse procedures often does not quantify uncertainty in the seismic moment tensor due to error in the Green’s functions from uncertain event location and Earth structure. We attempt to incorporate this uncertainty into an estimation of the seismic moment tensor using a distribution of velocity models calculated in a prior effort based on different and complementary data sets. The posterior distribution of velocity models is then used to construct Green’s functions for use in Bayesian inference of an unknown seismic moment tensor using regional waveform data. The combined likelihood is estimated using data-specific error models and the posterior of the seismic moment tensor is estimated and can be interpreted in terms of most-probable source-type.

58 GEOSCIENCES↗

NASA NTRS1992

Regions of negative Bz in the Tsyganenko 1989 model neutral sheet

A disturbing feature of the Tsyganenko (1989) model magnetic field, namely the occurrence of negative Bz in the model neutral sheet is pointed out. On the basis of observations of Bz in the neutral sheet it is concluded that this is an artifact of the model and not a real effect. This feature of the model should be considered when the model is used either to infer mappings from the ionosphere to the vicinity of the neutral sheet or as a tool in theoretical studies. It is proposed that in the development of future models, it would be useful for the distribution of Bz in the neutral sheet to be imposed as a constraint on the model.

Donovan, E. F.↗

DOE OSTI2025

Solving high-dimensional inverse problems using amortized likelihood-free inference with noisy and incomplete data

Here, we present a likelihood-free probabilistic inversion method based on normalizing flows for high-dimensional inverse problems. The proposed method is composed of two complementary networks: a summary network for data compression and an inference network for parameter estimation. The summary network encodes raw observations into a fixed-size vector of summary features, while the inference network generates samples of the approximate posterior distribution of the model parameters based on these summary features. The posterior samples are produced in a deep generative fashion by sampling from a latent Gaussian distribution and passing these samples through an invertible transformation. We construct this invertible transformation by sequentially alternating conditional invertible neural network and conditional neural spline flow layers. The summary and inference networks are trained simultaneously. We apply the proposed method to an inversion problem in groundwater hydrology to estimate the posterior distribution of the log-conductivity field conditioned on spatially sparse time-series observations of the system’s hydraulic head responses. The conductivity field is represented with 706 degrees of freedom in the considered problem. Comparison with the likelihood-based iterative ensemble smoother PEST-IES method demonstrates that the proposed method accurately estimates the parameter posterior distribution and the observations’ predictive posterior distribution at a fraction of the inference time of PEST-IES.

conditional invertible neural network↗

NASA NTRS2007

Evaluating the Capacity of Global CO2 Flux and Atmospheric Transport Models to Incorporate New Satellite Observations

As we enter the new era of satellite remote sensing for CO2 and other carbon cyclerelated quantities, advanced modeling and analysis capabilities are required to fully capitalize on the new observations. Model estimates of CO2 surface flux and atmospheric transport are required for initial constraints on inverse analyses, to connect atmospheric observations to the location of surface sources and sinks, and ultimately for future projections of carbon-climate interactions. For application to current, planned, and future remotely sensed CO2 data, it is desirable that these models are accurate and unbiased at time scales from less than daily to multi-annual and at spatial scales from several kilometers or finer to global. Here we focus on simulated CO2 fluxes from terrestrial vegetation and atmospheric transport mutually constrained by analyzed meteorological fields from the Goddard Modeling and Assimilation Office for the period 1998 through 2006. Use of assimilated meteorological data enables direct model comparison to observations across a wide range of scales of variability. The biospheric fluxes are produced by the CASA model at lxi degrees on a monthly mean basis, modulated hourly with analyzed temperature and sunlight. Both physiological and biomass burning fluxes are derived using satellite observations of vegetation, burned area (as in GFED-2), and analyzed meteorology. For the purposes of comparison to CO2 data, fossil fuel and ocean fluxes are also included in the transport simulations. In this presentation we evaluate the model's ability to simulate CO2 flux and mixing ratio variability in comparison to in situ observations at sites in Northern mid latitudes and the continental tropics. The influence of key process representations is inferred. We find that the model can resolve much of the hourly to synoptic variability in the observations, although there are limits imposed by vertical resolution of boundary layer processes. The seasonal cycle and its interannual variations generally respond adequately, but discrepancies in the tropics suggest the need for a refinement of the soil moisture dependence of the respiration flux in CASA. Examples and inferences for interpretation of satellite data will be discussed. In general, the fidelity of these simulations leads us to anticipate incorporation of real-time, highly resolved remote sensing and other observations into quantitative analyses that will reduce uncertainty in the terrestrial CO2 sink and revolutionize our understanding of the key processes controlling atmospheric CO2 and its evolution with time.

Kawa, S. R.↗