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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Efficient Nighttime Biogenic SOA Formation in a Polluted Residual Layer

Abstract Organic nitrates formed from nighttime reaction between anthropogenic nitrate radicals (NO3) and biogenic volatile organic compounds (BVOCs) are potentially an important but highly uncertain source of secondary organic aerosol (SOA). Here we report on the enhanced nighttime biogenic SOA formation observed in a polluted residual layer over Sacramento, California, the morning of 15 June 2010 during the Carbonaceous Aerosols and Radiative Effects Study (CARES). Trajectory analysis showed that the residual layer air, containing trace amounts of isoprene and monoterpenes left over after nighttime oxidation, was influenced by the San Francisco Bay Area emissions the previous evening. The residual layer aerosol was also enriched in nitrate, with about 64% of it estimated to be in the form of organic nitrates. The nitrate:organic mass ratio of the SOA was about 0.47±0.044, which corresponds to the range typically found in isoprene mononitrates. Assuming the SOA was composed of organic mononitrates, its nominal molecular weight was estimated at 186±11 g mol-1, consistent with the highly functionalized isoprene hydroxynitrates that have been observed in the particle phase in the southeast United States. Overall, our findings show that the efficiency of nighttime biogenic SOA formation, expressed as the change in organic aerosol mass relative to carbon monoxide (?OA/?CO), was about 100 µg m-3 ppmv, which falls in the range 77-157 µg m-3 ppmv previously estimated for enhanced daytime SOA formation from mixed anthropogenic and biogenic emissions during CARES.

Zaveri, Rahul A.↗
DOE OSTI2021

Projected Changes to Cool‐Season Storm Tides in the 21st Century Along the Northeastern United States Coast

Abstract This study investigates changes and uncertainties to cool‐season (November‐March) storm tides along the U.S. northeast coast in the 21st century under the high RCP8.5 emission scenario compared to late 20th century. A high‐fidelity (50‐m coastal resolution) hydrodynamic storm tide model is forced with three dynamically downscaled regional climate models (RCMs) over three decadal periods (historical, mid‐21st century, and late‐21st century) to project future changes in peak storm tide elevations at coastal counties in the region. While there is no absolute consensus on future changes to storm tides, for any one future decade two out of the three RCMs project an increase at counties along the Hudson River, Delaware River, and northern Chesapeake Bay due to more intense cyclones that track inland of these locations leading to favorable surge generating conditions. The same RCMs also project a decrease at counties facing the open ocean in the mid‐Atlantic Bight as cyclone densities just offshore of the coastline decrease, particularly by late‐century. The greater tidal range in northern areas of the region leads to uncertainty in the magnitude and sign of the projected changes to storm tide levels due to the arbitrary timing of the local tidal stage and the surge‐producing weather event. This random tide‐weather timing is less important in the Chesapeake Bay and unimportant in Albemarle Sound and Pamlico Sound. Similar to other recent studies, we highlight that sea level rise is likely to be more critical than storm climatology for future changes to the cool‐season coastal flooding potential.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Bayesian Model for HOPE Mass Spectrometers on Van Allen Probes

Space instruments rely heavily on modeling to predict and understand the instrument response, enabling a determination of the capabilities and resolution. The Bayesian approach provides a framework to incorporate prior knowledge and propagate uncertainty to predict the instrument response. We present an empirical Bayes model for the end-to-end performance of the Helium, Oxygen, Proton, and Electron mass spectrometers aboard the Van Allen Probes mission. In this model, we use a combination of external modeling, laboratory calibration, and expert opinion to construct the time-of-flight spectra and demonstrate good agreement with on-orbit data. The empirical Bayes model is applied to explore doubly charged ions and carbon, nitrogen, oxygen discrimination during the Van Allen Probes mission.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Probabilistic Evaluation of Geoscientific Hypotheses with Geophysical Data: Application to Electrical Resistivity Imaging of a Fractured Bedrock Zone

As climate changes and populations grow, groundwater sustainability is becoming increasingly important. Groundwater models, based on a conceptual understanding of the subsurface structure, are crucial tools for making sustainable management decisions. Conceptual models of the subsurface are based on knowledge of geological processes, and, frequently, observations from geophysical data. A frequent problem in groundwater model development occurs when multiple geological phenomena could explain a single subsurface observation. Uncertainty in geophysical data makes it even more difficult to discern which explanations are consistent with the geophysics. Here, we present a framework for testing geological when a geological feature is observed in geophysical data, but its physical characteristics are uncertain. The framework builds on Popper-Bayes methods developed in previous work, and is applied to study a fractured bedrock zone in a mountainous watershed in southwest Colorado. First, we outline six hypotheses based on the geological history of the watershed. Then, using the proposed Popper-Bayes approach, we demonstrate that three of the six hypotheses are inconsistent with measured electrical resistivity data, even after accounting for uncertainty. Finally, we discuss the importance of the prior model, and how this framework for handling geophysical uncertainty can be applied in other settings.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Interacting Effects of Watershed and Coastal Processes on the Evolution of Compound Flooding During Hurricane Irene

Abstract In low‐lying estuarine regions, compound flooding (CF) is caused by the co‐occurrence of extreme precipitation, river flooding and storm surge. In recent decades, there has been a rise in the frequency and intensity of pluvial‐coastal CF events in different parts of the U.S. due to the increased frequency of intense precipitation and storm surge events. However, in estuarine and deltaic regions, the CF characteristics depend mainly on the storm tide and river flow interaction. Understanding how the fluvial‐coastal CF may respond to changes to watershed and estuarine characteristics is essential for future CF hazard prediction. This study examined two critical processes: (a) the interplay between antecedent soil moisture conditions and peak river flow, and (b) how the impact of sea level rise (SLR) on storm surge and river flood distribution alters the CF in complex estuaries. As the study area, we selected the Delaware Bay and River, a shallow and convergent estuary in the US Mid‐Atlantic region—where flood hazards during a CF can become more significant than the surge and river flood processes occurring in isolation. For the focal event for the study, we selected Hurricane Irene (2011) because it reportedly produced the most extreme CF over the past two decades in the same region. Ultimately, our results illustrated that the potential changes to the catchment and bay characteristics from the global temperature increase and SLR could significantly modulate the fluvial‐coastal CF variability. The potential increase in global temperature and rainfall intensity might not always exacerbate the CF.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Can Restoring Tidal Wetlands Reduce Estuarine Nuisance Flooding of Coasts Under Future Sea‐Level Rise?

Wetland restoration is an increasingly popular nature‐based method for flood risk mitigation in coastal communities. In this study, we present a novel method using hydrodynamic modeling and harmonic analysis to quantify wetlands' ability to reduce future nuisance flooding. The method leverages a hydrodynamic model calibrated to present day data and was run for a range of future sea‐level rise (SLR) and wetland restoration scenarios to quantify changes to tidal harmonic amplitudes and phases. The harmonic constituents are used to generate water surface elevations over a time period of interest (e.g., one year) and compared to critical exceedance thresholds such as levee elevations. Then, changes to nuisance flooding are calculated by counting the number of hours critical thresholds are exceeded under different SLR and wetland restoration scenarios. We applied the method to Coos Bay, Oregon, USA as a test case. We found restoration reduces the number of hours nuisance flooding occurs in downtown Coos Bay from 15 hr (present day conditions) to 0 hr (fully restored condition) under median SLR (82 cm by 2100). Restoration had spatially variable impacts on reducing peak flood elevations with minimal impacts near the estuary mouth and greatest impact 32 km inland. The effectiveness of restoration was heavily dependent on future SLR. Restoration was maximally effective in 2050 under all SLR scenarios, less effective in 2100 under median SLR, and not effective under high SLR. Modeling results suggest increased tidal prism and accommodation space are driving restoration‐associated reductions in tidal amplitudes.

Brand, Matthew W. [Louisiana State Univ., Baton Ro↗
DOE OSTI2026

Investigating Coastal Vegetation Dynamics and Ecosystem Impacts Under Elevated CO 2 and Temperature: A Process‐Based Approach

Coastal forests are increasingly vulnerable to climate change and sea-level rise, with flooding and salinity driving transitions to marsh-dominated ecosystems. Using the coastal version of FATES-Hydro, we conducted 30-year simulations at two coastal forest sites—a broadleaf swamp white oak stand at Lake Erie and a conifer loblolly pine stand at Chesapeake Bay—under historical climate and elevated CO 2 (+100 ppm) and temperature (+1.5°C) scenarios. Elevated CO 2 increased net primary productivity at both sites, while warming alone intensified hydraulic stress and accelerated mortality, particularly in the conifer stand. Simulations show that elevated temperatures intensify vapor pressure deficit and hydraulic stress on trees already experiencing salinity- and submersion-driven water stress, increasing tree mortality beyond what would be expected in a non-water-limited environment. Marsh expansion partially compensated for tree loss at the Lake Erie site but reduced ecosystem productivity in the conifer forest at Chesapeake Bay. In conclusion, our results highlight how differences in stand structure, phenology, and local hydrology modulate ecosystem trajectories under climate change, emphasizing the importance of demographic and community-level processes for predicting the fate of coastal forests.

Ding, Junyan [Barcelona Supercomputing Center (BSC↗
DOE OSTI2023

Climate-driven tradeoffs between landscape connectivity and the maintenance of the coastal carbon sink

Abstract Ecosystem connectivity tends to increase the resilience and function of ecosystems responding to stressors. Coastal ecosystems sequester disproportionately large amounts of carbon, but rapid exchange of water, nutrients, and sediment makes them vulnerable to sea level rise and coastal erosion. Individual components of the coastal landscape (i.e., marsh, forest, bay) have contrasting responses to sea level rise, making it difficult to forecast the response of the integrated coastal carbon sink. Here we couple a spatially-explicit geomorphic model with a point-based carbon accumulation model, and show that landscape connectivity, in-situ carbon accumulation rates, and the size of the landscape-scale coastal carbon stock all peak at intermediate sea level rise rates despite divergent responses of individual components. Progressive loss of forest biomass under increasing sea level rise leads to a shift from a system dominated by forest biomass carbon towards one dominated by marsh soil carbon that is maintained by substantial recycling of organic carbon between marshes and bays. These results suggest that climate change strengthens connectivity between adjacent coastal ecosystems, but with tradeoffs that include a shift towards more labile carbon, smaller marsh and forest extents, and the accumulation of carbon in portions of the landscape more vulnerable to sea level rise and erosion.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Extreme flood return levels in a U.S. mid-Atlantic estuary using 40-year fluvial-coastal model simulations

Using an integrated watershed-coastal modeling framework, we conducted long-term historical simulations (1980-2019) of fluvial and coastal flooding in the Delaware Bay and River, a vulnerable estuarine system in the U.S., at high spatial resolutions. By applying process-based models, we generated physically consistent and spatially detailed estimates of estuarine, riverine, and surge-driven extreme water level compared to previous studies that used field data only. We then evaluated changes in the magnitude of flood events using the 40-year simulations and detrended Floodwater Depth values with stationary extreme value analysis. Our detailed assessment of spatial-varying extreme values revealed how different flood-generation mechanisms can dominate various zones in the estuary. The datasets produced through this work will be valuable for long-term flood hazard mitigation planning in coastal communities in the Delaware Bay and River region. Additionally, this work will serve as a benchmark for other coastal flood hazard modeling communities worldwide, aiding them in systematically modeling long-term and continuous extreme flood events.

Coastal and Riverine Flooding↗
DOE OSTI2021

Utilizing distributed acoustic sensing and ocean bottom fiber optic cables for submarine structural characterization

The sparsity of permanent seismic instrumentation in marine environments often limits the availability of subsea information on geohazards, including active fault systems, in both time and space. One sensing resource that provides observational access to the seafloor environment are existing networks of ocean bottom fiber optic cables; these cables, coupled to modern distributed acoustic sensing (DAS) systems, can provide dense arrays of broadband seismic observations capable of recording both seismic events and the ambient noise wavefield. Here, we report a marine DAS application which demonstrates the strength and limitation of this new technique on submarine structural characterization. Based on ambient noise DAS records on a 20 km section of a fiber optic cable offshore of Moss Landing, CA, in Monterey Bay, we extract Scholte waves from DAS ambient noise records using interferometry techniques and invert the resulting multimodal dispersion curves to recover a high resolution 2D shear-wave velocity image of the near seafloor sediments. We show for the first time that the migration of coherently scattered Scholte waves observed on DAS records can provide an approach for resolving sharp lateral contrasts in subsurface properties, particularly shallow faults and depositional features near the seafloor. Our results provide improved constraints on shallow submarine features in Monterey Bay, including fault zones and paleo-channel deposits, thus highlighting one of many possible geophysical uses of the marine cable network.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Bayesian Monte Carlo Evaluation of Imperfect (n, 233 U) Data and Model

Conventional nuclear data evaluation methods using generalized linear least squares make the following assumptions: prior and posterior probability distribution functions (PDFs) of all model parameters and data are normal (Gaussian); the linear approximation is sufficiently accurate to minimize the cost function (even for nonlinear models); the model (e.g., of neutron cross section) and experimental data (including covariance data) are without defect and prior PDFs of parameters and measured data are known perfectly. Neglect of covariance between model parameters and measured data in conventional evaluations contributes to imperfections. These assumptions are inherent to the generalized linear least squares minimization method commonly used for resolved resonance region neutron cross section evaluations but are often not justified due to the presence of non-normal PDFs, nonlinear models (e.g., R-matrix formalism), and inherent imperfections in data and models (e.g., imperfect covariance data). Here, these assumptions are removed in a mathematical framework of Bayes’ theorem, which is implemented using the Metropolis-Hastings Monte Carlo method. Most importantly, new parameters are introduced to parameterize discrepancies between the theoretical model and measured data to quantify judgement about discrepancies or imperfections in a reproducible manner. An evaluation of 233U in the eV region using the ENDF-B/VIII.0 library and transmission data (Guber et al.) is presented, and posterior parameters are compared to those obtained by conventional evaluation methods. This example illustrates the effects of removing the most harmful assumption: that of model-data perfection.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Mixing and dilution controls on marine CO 2 removal using alkalinity enhancement

Marine CO 2 removal (CDR) using enhanced-alkalinity seawater discharge was simulated in the estuarine waters of the Salish Sea, Washington, US. The high-alkalinity seawater would be generated using bipolar membrane electrodialysis technology to remove acid and the alkaline stream returned to the sea. Response of the receiving waters was evaluated using a shoreline resolving hydrodynamic model with biogeochemistry, and carbonate chemistry. Two sites, and two deployment scales, each with enhanced TA of 2997 mmol m -3 and a pH of 9 were simulated. The effects on air-sea CO 2 flux and pH in the near-field as well as over the larger estuary wide domain were assessed. The large-scale deployment (addition of 164 Mmoles TA yr -1 ) in a small embayment (Sequim Bay, 12.5 km 2 ) resulted in removal of 2066 T of CO 2 (45% of total simulated) at rate of 3756 mmol m -2 yr -1 , higher than the 63 mmol m -2 yr -1 required globally to remove 1.0 GT CO 2 yr -1 . It also reduced acidity in the bay, ΔpH ≈ +0.1 pH units, an amount comparable to the historic impacts of anthropogenic acidification in the Salish Sea. The mixing and dilution of added TA with distance from the source results in reduced CDR rates such that comparable amount 2176 T CO 2 yr -1 was removed over >1000 fold larger area of the rest of the model domain. There is the potential for more removal occurring beyond the region modeled. The CDR from reduction of outgassing between October and May accounts for as much as 90% of total CDR simulated. Of the total, only 375 T CO 2 yr -1 (8%) was from the open shelf portion of the model domain. With shallow depths limiting vertical mixing, nearshore estuarine waters may provide a more rapid removal of CO 2 using alkalinity enhancement relative to deeper oceanic sites.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Periodicity significance testing with null-signal templates: reassessment of PTF’s SMBH binary candidates

Periodograms are widely employed for identifying periodicity in time series data, yet they often struggle to accurately quantify the statistical significance of detected periodic signals when the data complexity precludes reliable simulations. We develop a data-driven approach to address this challenge by introducing a null-signal template (NST). The NST is created by carefully randomizing the period of each cycle in the periodogram template, rendering it non-periodic. It has the same frequentist properties as a periodic signal template, and we show with simulations that the distribution of false positives is the same as with the original periodic template, regardless of the underlying data. Thus, performing a periodicity search with the NST acts as an effective simulation of the null (no-signal) hypothesis, without having to simulate the noise properties of the data. We apply the NST method to the supermassive black hole binaries (SMBHB) search in the Palomar Transient Factory (PTF), where Charisi et al. had previously proposed 33 high signal-to-noise candidates utilizing simulations to quantify their significance. Our approach reveals that these simulations do not capture the complexity of the real data. There are no statistically significant periodic signal detections above the non-periodic background. To improve the search sensitivity, we introduce a Gaussian quadrature based algorithm for the Bayes Factor with correlated noise as a test statistic. We show with simulations that this improves sensitivity to true signals by more than an order of magnitude. However, the Bayes Factor approach also results in no statistically significant detections in the PTF data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Determination of proton PDF uncertainties with Markov chain Monte Carlo

We present an analysis of parton distribution functions (PDFs) of the proton using Markov chain Monte Carlo (MCMC) methods. The MCMC approach naturally implements Bayes’ theorem and, thus, provides a means to directly sample the underlying probability distribution—in this case, the probability distribution of the PDF parameters. This allows for a straightforward propagation of the resulting uncertainties into any PDF-dependent observable, preserving their simple probabilistic interpretation. In our analysis we include a broad set of deep inelastic scattering data from HERA, BCDMS and NMC experiments along with the Drell-Yan, 𝑊 and 𝑍 boson data from LHC and Tevatron experiments, which combined with theoretical calculations at next-to-next-to-leading order in QCD allow for realistic determination of PDFs. The main focus of this analysis is to explore alternative methods for PDF uncertainty estimation that are more firmly grounded in statistical principles. We show that the flexibility of the Bayes framework, allowing one, e.g., to account for non-Gaussianity or inconsistencies of datasets, is crucial to extract realistic uncertainties when such assumptions are not fulfilled. We also demonstrate that MCMC allows one to determine the Δ⁢𝜒 2 value corresponding to a given confidence level in the sample, which can, in turn, be used as a statistically well-founded tolerance criterion used in the Hessian method, thus addressing one of its main long-standing drawbacks.

Risse, Peter Clemens [Universität Münster (Germany↗
DOE OSTI2026

Electromagnetic Analysis of ITER Electron Cyclotron Emission Model

The ITER electron cyclotron emission (ECE) diagnostic system is located at diagnostics shielding module 2 (DSM2), equatorial port 9 (EP9), to measure electron temperature profile and electron temperature fluctuations, and also assess nonthermal electron distributions via the oblique view. Therefore, ECE has both radial and oblique views with two hot sources for calibration and two couples of mirrors for two different optical views. It shall receive that the large electromagnetic (EM) loads up to 100-MN/m3 force density due to the eddy currents on the copper mirrors generated by the short transient plasma disruption. The simplified EM analysis model using magnetic field ( B ) data and flux variations (dB/dt) method based on the worst case of plasma disruption, MD_DW_EXP16MS_CATIII, has been developed to calculate the force and moments for each ECE component and support structure for EP9 DSM2 Bay2 and Bay3. The B and dB/dt methods use the constant B and dB/dt assumed for each local bay space, but the real B and dB/dt are basically varied along the radial direction of each bay. The complex global model with the local ECE components and support structure using Maxwell transient has been developed to do EM analysis to compare the results of the B and dB/dt models. The volumetric EM force density of the whole ECE components and support structure can be used for the subsequent structural analysis. In conclusion, combined with the thermal and nuclear loads and seismic and initial loads, the ECE integration analysis can be finalized for the operation case.

Fang, J. [Princeton Plasma Physics Laboratory (PPP↗
DOE OSTI2025

Hybridization capture sequencing for Vibrio spp. and associated virulence factors

ABSTRACT Proliferation ofVibriospp. in aquatic ecosystems is associated with climate change and, concomitantly, increased incidence of vibriosis. They are autochthonous to aquatic environments globally, but traditional metagenomic methods for detecting and typing pathogenicVibriospp. are challenged by their presence in relatively low abundance and ability to persist in a viable but nonculturable state. In the study reported here, hybridization capture sequencing (HCS) was employed to profile low-abundanceVibriospp. in environmental samples. The HCS panel targeted a family of molecular chaperones (CPN60) specific to 69Vibriospp. and 162Vibrio-specific virulence factors. This approach was evaluated in parallel with traditional whole-community shotgun sequencing in a metagenomic analysis of water and oyster samples collected from the Chesapeake Bay. In addition,Vibrio parahaemolyticusandVibrio vulnificusstrains isolated from the samples were subjected to whole-genome sequencing to determine the genetic characteristics of pathogenicVibriospp. circulating in an aquatic environment. HCS, employed to determine the incidence and characterization of specificVibriospp., yielded significantly greater metagenomic insight, notably a variety of otherVibriospp., including detection ofVibrio cholerae,Vibrio fluvialis, andVibrio aestuarianus, in addition toVibrio parahaemolyticusandVibrio vulnificus, and also important virulence factors not detectable using traditional molecular methods. Thus, pathogenicVibriospp. in aquatic ecosystems may be far more common than currently understood. It is concluded that environmental surveillance should include HCS, a valuable tool for the detection and characterization of pathogenic agents in aquatic ecosystems, notably vibrios. IMPORTANCE The increasing prevalence of pathogenicVibriospp. in aquatic ecosystems, driven by climate change, is closely linked to a rise in cholera and vibriosis cases, emphasizing the need for improved environmental surveillance. Vibrios are naturally occurring in aquatic environments globally, but traditional metagenomic methods for detecting and typing pathogenicVibriospp. are challenged by their presence in relatively low abundance and ability to persist in a viable but nonculturable state. In the study reported here, hybridization capture sequencing was employed to profile low-abundanceVibriospp. in metagenomic samples, namely water and oysters collected from the Chesapeake Bay. This approach was evaluated in parallel with traditional whole-community shotgun sequencing and whole-genome sequencing ofVibrio parahaemolyticusandVibrio vulnificusstrains isolated from the samples. Results suggest pathogenicVibriospp. in aquatic ecosystems may be far more common than currently understood, when multiple methods are considered for environmental surveillance.

Microbiology↗
DOE OSTI2023

Simulating the Impact of Dynamic Rerouting on Metropolitan-scale Traffic Systems

The rapid introduction of mobile navigation aides that use real-time road network information to suggest alternate routes to drivers is making it more difficult for researchers and government transportation agencies to understand and predict the dynamics of congested transportation systems. Computer simulation is a key capability for these organizations to analyze hypothetical scenarios; however, the complexity of transportation systems makes it challenging for them to simulate very large geographical regions, such as multi-city metropolitan areas. In this article, we describe enhancements to the Mobiliti parallel traffic simulator to model dynamic rerouting behavior with the addition of vehicle controller actors and vehicle-to-controller reroute requests. The simulator is designed to support distributed-memory parallel execution using discrete event simulation and be scalable on high-performance computing platforms. We demonstrate the potential of the simulator by analyzing the impact of varying the population penetration rate of dynamic rerouting on the San Francisco Bay Area road network. Using high-performance parallel computing, we can simulate a day in the San Francisco Bay Area with 19 million vehicle trips with 50 percent dynamic rerouting penetration over a road network with 0.5 million nodes and 1 million links in less than three minutes. We present a sensitivity study on the dynamic rerouting parameters, discuss the simulator’s parallel scalability, and analyze system-level impacts of changing the dynamic rerouting penetration. Furthermore, we examine the varying effects on different functional classes and geographical regions and present a validation of the simulation results compared to real-world data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

mmbo (Multi-modal Bayesian Optimization) [SWR-25-119]

This package implements Bayesian Neural Network (BNN) based surrogate models for multi-modal data. These models are fit using a custom Variational Bayes procedure leveraging conjugate posteriors in the last layer of the network, offering more accurate predictions with better-calibrated uncertainty quantification than mean-field Variational Bayes.

Taylor, Ian [National Laboratory of the Rockies (N↗