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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Producing High-fidelity Synthetic Population Ensembles at Scale

Used within social simulations, synthetic population ensembles enable uncertainty quantification (UQ) methods for obtaining more robust model inference and prediction. A synthetic population ensemble is a series of plausible virtual reconstructions of an area’s population at the granularity of people and residences, generated stochastically to preserve privacy of the source population survey’s respondents. In this paper, we demonstrate the production of large synthetic population ensembles for the US via Oak Ridge National Laboratory’s UrbanPop framework to support modeling of high spatial resolution energy affordability metrics from nationwide social surveys in collaboration with the fusionACS project. Our initial task involves creating ensembles for 17 US metropolitan areas, each consisting of 41 population instances (a base realization and 40 replicates). To accomplish this task at scale, we configured an integrated system comprised of a research cloud, virtual containerization, GPU-enhanced functionality, and a dual API/CLI to interact with UrbanPop’s maturing Likeness Python ecosystem. We observe a reduction in theoretical execution time while maintaining high-fidelity approximations of residential totals by metropolitan area and the demographic characteristics of neighborhoods. We discuss expansion of our approach to produce synthetic population ensembles for the entire US, particularly plans to establish automated workflows for job orchestration to increase computational efficiency, as well as provide outlook for broadening applications of the ensembles.

Gaboardi, James [ORNL] (ORCID:0000000247766826)↗
DOE OSTI2018

Bayesian Hierarchical Models for Parameter Inference with Missing Data: Supernova Cosmology Case Study [Slides]

Large scale astronomical surveys are going wider and deeper than ever before. However, astronomers, cosmologists and theorists continue to face the perennial issue that their data sets are often incomplete in magnitude space and must be carefully treated in order to avoid Malmquist bias, especially in the field of supernova cosmology. Historically, cosmological parameter inference in supernova cosmology was done using χ2 methodology; however, recent years have seen a rise in the use of Bayesian Hierarchical Models. In this presentation we develop a Bayesian Hierarchical methodology to account for magnitude limited surveys and present a specific application to cosmological parameter inference and model selection in supernova cosmology.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Are GRACE-era Terrestrial Water Trends Driven by Anthropogenic Climate Change?

To provide context for observed trends in terrestrial water storage (TWS) during GRACE (2003–2014), trends and variability in the CESM1-CAM5 Large Ensemble (LE) are examined. Motivated in part by the anomalous nature of climate variability during GRACE, the characteristics of both forced change and internal modes are quantified and their influences on observations are estimated. Trends during the GRACE era in the LE are dominated by internal variability rather than by the forced response, with TWS anomalies in much of the Americas, eastern Australia, Africa, and southwestern Eurasia largely attributable to the negative phases of the Pacific Decadal Oscillation (PDO) and Atlantic Multidecadal Oscillation (AMO). While similarities between observed trends and the model-inferred forced response also exist, it is inappropriate to attribute such trends mainly to anthropogenic forcing. For several key river basins, trends in the mean state and interannual variability and the time at which the forced response exceeds background variability are also estimated while aspects of global mean TWS, including changes in its annual amplitude and decadal trends, are quantified. The findings highlight the challenge of detecting anthropogenic climate change in temporally finite satellite datasets and underscore the benefit of utilizing models in the interpretation of the observed record.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Statistical characterization of experimental magnetized liner inertial fusion stagnation images using deep-learning-based fuel–background segmentation

Significant variety is observed in spherical crystal x-ray imager (SCXI) data for the stagnated fuel–liner system created in Magnetized Liner Inertial Fusion (MagLIF) experiments conducted at the Sandia National Laboratories Z-facility. As a result, image analysis tasks involving, e.g., region-of-interest selection (i.e. segmentation), background subtraction and image registration have generally required tedious manual treatment leading to increased risk of irreproducibility, lack of uncertainty quantification and smaller-scale studies using only a fraction of available data. We present a convolutional neural network (CNN)-based pipeline to automate much of the image processing workflow. This tool enabled batch preprocessing of an ensemble of N scans = 139 SCXI images across N exp = 67 different experiments for subsequent study. The pipeline begins by segmenting images into the stagnated fuel and background using a CNN trained on synthetic images generated from a geometric model of a physical three-dimensional plasma. The resulting segmentation allows for a rules-based registration. Our approach flexibly handles rarely occurring artifacts through minimal user input and avoids the need for extensive hand labelling and augmentation of our experimental dataset that would be needed to train an end-to-end pipeline. Here we also fit background pixels using low-degree polynomials, and perform a statistical assessment of the background and noise properties over the entire image database. Our results provide a guide for choices made in statistical inference models using stagnation image data and can be applied in the generation of synthetic datasets with realistic choices of noise statistics and background models used for machine learning tasks in MagLIF data analysis. We anticipate that the method may be readily extended to automate other MagLIF stagnation imaging applications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1994

Galactic dual population models of gamma-ray bursts

We investigate in more detail the properties of two-population models for gamma-ray bursts in the galactic disk and halo. We calculate the gamma-ray burst statistical properties, mean value of (V/V(sub max)), mean value of cos Theta, and mean value of (sin(exp 2) b), as functions of the detection flux threshold for bursts coming from both Galactic disk and massive halo populations. We consider halo models inferred from the observational constraints on the large-scale Galactic structure and we compare the expected values of mean value of (V/V(sub max)), mean value of cos Theta, and mean value of (sin(exp 2) b), with those measured by Burst and Transient Source Experiment (BATSE) and other detectors. We find that the measured values are consistent with solely Galactic populations having a range of halo distributions, mixed with local disk distributions, which can account for as much as approximately 25% of the observed BATSE bursts. M31 does not contribute to these modeled bursts. We also demonstrate, contrary to recent arguments, that the size-frequency distributions of dual population models are quite consistent with the BATSE observations.

Higdon, J. C.↗
DOE OSTI2016

A disk-based dynamical constraint on the mass of the young binary DQ Tau

We present new Atacama Large Millimeter/Submillimeter Array (ALMA) observations of CO J = 2−1 line emission from the DQ Tau circumbinary disk. These data are used to tomographically reconstruct the Keplerian disk velocity field in a forward-modeling inference framework, and thereby provide a dynamical constraint on the mass of the DQ Tau binary of M{sub ∗}=1.27{sub −0.27}{sup +0.46} M{sub ⊙}. Those results are compared with an updated and improved orbital solution for this double-lined system based on long-term monitoring of its stellar radial velocities. Both of these independent dynamical constraints on the binary mass are in excellent agreement: taken together, they demonstrate that the DQ Tau system mass is 1.21 ± 0.26 M{sub ⊙} and that the disk and binary orbital planes are aligned within 3° (at 3σ confidence). The predictions of various theoretical models for pre-main-sequence stellar evolution are also consistent with these masses, though more detailed comparisons are difficult due to lingering uncertainties regarding the photospheric properties of the individual components. DQ Tau is the third, nearly equal-mass, double-lined spectroscopic binary with a circumbinary disk that has been dynamically “weighed” with these two independent techniques: all show consistent results, validating the overall accuracy of the disk-based approach and demonstrating that it can be robustly applied to large samples of young, single stars as ALMA ramps up to operations at full capacity.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

The clustering of the SDSS-IV extended Baryon Oscillation Spectroscopic Survey DR14 quasar sample: structure growth rate measurement from the anisotropic quasar power spectrum in the redshift range 0.8 < z < 2.2

We analyse the clustering of the Sloan Digital Sky Survey IV extended Baryon Oscillation Spectroscopic Survey Data Release 14 quasar sample (DR14Q).We measure the redshift space distortions using the power-spectrum monopole, quadrupole, and hexadecapole inferred from 148 659 quasars between redshifts 0.8 and 2.2, covering a total sky footprint of 2112.9 deg 2 . We constrain the logarithmic growth of structure times the amplitude of dark matter density fluctuations, fσ 8 , and the Alcock–Paczynski dilation scales that allow constraints to be placed on the angular diameter distance D A (z) and the Hubble H(z) parameter. At the effective redshift of z eff = 1.52, fσ 8 (z eff ) = 0.420 ± 0.076, H(z eff ) = [162 ± 12] (r$fid\atop{s}$ /r s ) kms -1 Mpc -1 , and DA(z eff ) = [1.85 ± 0.11] × 10 3 (r s/ r$fid\atop{s}$) Mpc, where r s is the comoving sound horizon at the baryon drag epoch and the superscript ‘fid’ stands for its fiducial value. The errors take into account the full error budget, including systematics and statistical contributions. These results are in full agreement with the current Λ-Cold Dark Matter cosmological model inferred from Planck measurements. Finally, we compare our measurements with other eBOSS companion papers and find excellent agreement, demonstrating the consistency and complementarity of the different methods used for analysing the data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Generic inference of inflation models by non-Gaussianity and primordial power spectrum reconstruction

We present a generic inference method for inflation models from observational data by the usage of higher-order statistics of the curvature perturbation on uniform density hypersurfaces. This method is based on the calculation of the posterior for the primordial non-Gaussianity parameters f{sub NL} and g{sub NL}, which in general depend on specific parameters of inflation and reheating models, and enables to discriminate among the still viable inflation models. To keep analyticity as far as possible to dispense with numerically expensive sampling techniques a saddle-point approximation is introduced, whose precision is validated for a numerical toy example. The mathematical formulation is done in a generic way so that the approach remains applicable to cosmic microwave background data as well as to large scale structure data. Additionally, we review a few currently interesting inflation models and present numerical toy examples thereof in two and three dimensions to demonstrate the efficiency of the higher-order statistics method. A second quantity of interest is the primordial power spectrum. Here, we present two Bayesian methods to infer it from observational data, the so called critical filter and an extension thereof with smoothness prior, both allowing for a non-parametric spectrum reconstruction. These methods are able to reconstruct the spectra of the observed perturbations and the primordial ones of curvature perturbation even in case of non-Gaussianity and partial sky coverage. We argue that observables like T- and B-modes permit to measure both spectra. This also allows to infer the level of non-Gaussianity generated since inflation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

RAM‐SCB simulations of electron transport and plasma wave scattering during the October 2012 “double‐dip” storm

Abstract Mechanisms for electron injection, trapping, and loss in the near‐Earth space environment are investigated during the October 2012 “double‐dip” storm using our ring current‐atmosphere interactions model with self‐consistent magnetic field (RAM‐SCB). Pitch angle and energy scattering are included for the first time in RAM‐SCB using L and magnetic local time (MLT)‐dependent event‐specific chorus wave models inferred from NOAA Polar‐orbiting Operational Environmental Satellites (POES) and Van Allen Probes Electric and Magnetic Field Instrument Suite and Integrated Science observations. The dynamics of the source (approximately tens of keV) and seed (approximately hundreds of keV) populations of the radiation belts simulated with RAM‐SCB is compared with Van Allen Probes Magnetic Electron Ion Spectrometer observations in the morning sector and with measurements from NOAA 15 satellite in the predawn and afternoon MLT sectors. We find that although the low‐energy ( E < 100 keV) electron fluxes are in good agreement with observations, increasing significantly by magnetospheric convection during both SYM‐H dips while decreasing during the intermediate recovery phase, the injection of high‐energy electrons is underestimated by this mechanism throughout the storm. Local acceleration by chorus waves intensifies the electron fluxes at E ≥50 keV considerably, and RAM‐SCB simulations overestimate the observed trapped fluxes by more than an order of magnitude; the precipitating fluxes simulated with RAM‐SCB are weaker, and their temporal and spatial evolutions agree well with POES/Medium Energy Proton and Electron Detectors data.

58 GEOSCIENCES↗
DOE OSTI2023

Mapping Glacier Basal Sliding Applying Machine Learning

During the RESOLVE project (“High-resolution imaging in subsurface geophysics: development of a multi-instrument platform for interdisciplinary research”), continuous surface displacement and seismic array observations were obtained on Glacier d’Argentière in the French Alps for 35 days in May 2018. The data set is used to perform a detailed study of targeted processes within the highly dynamic cryospheric environment. In particular, the physical processes controlling glacial basal motion are poorly understood and remain challenging to observe directly. Especially in the Alpine region for temperate based glaciers where the ice rapidly responds to changing climatic conditions and thus, processes are strongly intermittent in time and heterogeneous in space. Spatially dense seismic and Global Positioning System (GPS) measurements are analyzed applying machine learning to gain insight into the processes controlling glacial motions of Glacier d’Argentière. Using multiple bandpass-filtered copies of the continuous seismic waveforms, we compute energy-based features, develop a matched field beamforming catalog and include meteorological observations. Features describing the data are analyzed with a gradient boosting decision tree model to directly estimate the GPS displacements from the seismic noise. We posit that features of the seismic noise provide direct access to the dominant parameters that drive displacement on the highly variable and unsteady surface of the glacier. The machine learning model infers daily fluctuations and longer term trends. The results show on-ice displacement rates are strongly modulated by activity at the base of the glacier. The techniques presented provide a new approach to study glacial basal sliding and discover its full complexity.

58 GEOSCIENCES↗
DOE OSTI2019

Chronic gamma radiation resistance in fungi correlates with resistance to chromium and elevated temperatures, but not with resistance to acute irradiation

Abstract Exposure to chronic ionizing radiation (CIR) from nuclear power plant accidents, acts of terrorism, and space exploration poses serious threats to humans. Fungi are a group of highly radiation-resistant eukaryotes, and an understanding of fungal CIR resistance mechanisms holds the prospect of protecting humans. We compared the abilities of 95 wild-type yeast and dimorphic fungal isolates, representing diverse Ascomycota and Basidiomycota , to resist exposure to five environmentally-relevant stressors: CIR (long-duration growth under 36 Gy/h) and acute (10 kGy/h) ionizing radiation (IR), heavy metals (chromium, mercury), elevated temperature (up to 50 °C), and low pH (2.3). To quantify associations between resistances to CIR and these other stressors, we used correlation analysis, logistic regression with multi-model inference, and customized machine learning. The results suggest that resistance to acute IR in fungi is not strongly correlated with the ability of a given fungal isolate to grow under CIR. Instead, the strongest predictors of CIR resistance in fungi were resistance to chromium (III) and to elevated temperature. These results suggest fundamental differences between the mechanisms of resistance to chronic and acute radiation. Convergent evolution towards radioresistance among genetically distinct groups of organisms is considered here.

Shuryak, Igor↗
DOE OSTI2014

Study of heat sources interacting in integrated circuits by laser mirage effect

This work exploits the mirage effect to analyze multiple heat sources thermally interacting in an integrated circuit (IC) by means of a probe IR laser beam, which strikes on the die lateral walls and passes through the die substrate. Under such conditions, the criteria for locating such hot spots, as well as their relative power dissipation, are discussed on the basis of a theoretical model inferred in this work. Finally, the technique feasibility is shown in a real application scenario, obtaining 5-μm spatial lateral resolution and an error in power dissipation measurements below 5%. This method may become a practical alternative to usual off-chip techniques for inspecting hot spots in ICs and to experimentally characterize heat flow in the semiconductor substrate.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Thermonuclear neutron emission from a sheared-flow stabilized Z-pinch

We report the fusion Z-pinch experiment (FuZE) is a sheared-flow stabilized Z-pinch designed to study the effects of flow stabilization on deuterium plasmas with densities and temperatures high enough to drive nuclear fusion reactions. Results from FuZE show high pinch currents and neutron emission durations thousands of times longer than instability growth times. While these results are consistent with thermonuclear neutron emission, energetically resolved neutron measurements are a stronger constraint on the origin of the fusion production. This stems from the strong anisotropy in energy created in beam-target fusion, compared to the relatively isotropic emission in thermonuclear fusion. In dense Z-pinch plasmas, a potential and undesirable cause of beam-target fusion reactions is the presence of fast-growing, “sausage” instabilities. This work introduces a new method for characterizing beam instabilities by recording individual neutron interactions in plastic scintillator detectors positioned at two different angles around the device chamber. Histograms of the pulse-integral spectra from the two locations are compared using detailed Monte Carlo simulations. These models infer the deuteron beam energy based on differences in the measured neutron spectra at the two angles, thereby discriminating beam-target from thermonuclear production. An analysis of neutron emission profiles from FuZE precludes the presence of deuteron beams with energies greater than 4.65 keV with a statistical uncertainty of 4.15 keV and a systematic uncertainty of 0.53 keV. This analysis demonstrates that axial, beam-target fusion reactions are not the dominant source of neutron emission from FuZE. These data are promising for scaling FuZE up to fusion reactor conditions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

Tracing the cosmic metal evolution in the low-redshift intergalactic medium

Using the Cosmic Origins Spectrograph aboard the Hubble Space Telescope, we measured the abundances of six ions (C III, C IV, Si III, Si IV, N V, and O VI) in the low-redshift (z ≤ 0.4) intergalactic medium (IGM). Both C IV and Si IV have increased in abundance by a factor of ∼10 from z ≈ 5.5 to the present. We derive ion mass densities, ρ{sub ion} ≡ Ω{sub ion}ρ{sub cr}, with Ω{sub ion} expressed relative to the closure density. Our models of mass-abundance ratios, (Si III/Si IV) =0.67{sub −0.19}{sup +0.35}, (C III/C IV) =0.70{sub −0.20}{sup +0.43}, and (Ω{sub C} {sub III}+Ω{sub C} {sub IV})/(Ω{sub Si} {sub III}+Ω{sub Si} {sub IV})=4.9{sub −1.1}{sup +2.2}, are consistent with the photoionization parameter log U = –1.5 ± 0.4, hydrogen photoionization rate Γ{sub H} = (8 ± 2) × 10{sup –14} s{sup –1} at z < 0.4, and specific intensity I {sub 0} = (3 ± 1) × 10{sup –23} erg cm{sup –2} s{sup –1} Hz{sup –1} sr{sup –1} at the Lyman limit. Consistent ionization corrections for C and Si are scaled to an ionizing photon flux Φ{sub 0} = 10{sup 4} cm{sup –2} s{sup –1}, baryon overdensity Δ {sub b} ≈ 200 ± 50, and ''alpha-enhancement'' (Si/C enhanced to three times its solar ratio). We compare these metal abundances to the expected IGM enrichment and abundances in higher photoionized states of carbon (C V) and silicon (Si V, Si VI, and Si VII). Our ionization modeling infers IGM metal densities of (5.4 ± 0.5) × 10{sup 5} M {sub ☉} Mpc{sup –3} in the photoionized Lyα forest traced by the C and Si ions and (9.1 ± 0.6) × 10{sup 5} M {sub ☉} Mpc{sup –3} in hotter gas traced by O VI. Combining both phases, the heavy elements in the IGM have mass density ρ {sub Z} = (1.5 ± 0.8) × 10{sup 6} M {sub ☉} Mpc{sup –3} or Ω {sub Z} ≈ 10{sup –5}. This represents 10% ± 5% of the metals produced by (6 ± 2) × 10{sup 8} M {sub ☉} Mpc{sup –3} of integrated star formation with yield y{sub m} = 0.025 ± 0.010. The missing metals at low redshift may reside within galaxies and in undetected ionized gas in galactic halos and circumgalactic medium.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Predicting resistive wall mode stability in NSTX through balanced random forests and counterfactual explanations

Abstract Recent progress in the disruption event characterization and forecasting framework has shown that machine learning guided by physics theory can be easily implemented as a supporting tool for fast computations of ideal stability properties of spherical tokamak plasmas. In order to extend that idea, a customized random forest (RF) classifier that takes into account imbalances in the training data is hereby employed to predict resistive wall mode (RWM) stability for a set of high beta discharges from the NSTX spherical tokamak. More specifically, with this approach each tree in the forest is trained on samples that are balanced via a user-defined over/under-sampler. The proposed approach outperforms classical cost-sensitive methods for the problem at hand, in particular when used in conjunction with a random under-sampler, while also resulting in a threefold reduction in the training time. In order to further understand the model’s decisions, a diverse set of counterfactual explanations based on determinantal point processes (DPP) is generated and evaluated. Via the use of DPP, the underlying RF model infers that the presence of hypothetical magnetohydrodynamic activity would have prevented the RWM from concurrently going unstable, which is a counterfactual that is indeed expected by prior physics knowledge. Given that this result emerges from the data-driven RF classifier and the use of counterfactuals without hand-crafted embedding of prior physics intuition, it motivates the usage of counterfactuals to simulate real-time control by generating the β N levels that would have kept the RWM stable for a set of unstable discharges.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

wavess 1.2: presenting an HLA-aware within-host virus sequence simulation framework

Motivation Understanding how virus sequences are shaped by selection can inform vaccine design and transmission inference. Modeling within-host evolution to interrogate these questions requires a detailed mechanistic framework that accurately captures sequence diversification. The CD8 + cytotoxic T-lymphocyte (CTL) response plays an important role in immune-mediated selection and can leave strong signatures in virus sequences; however, existing sequence-based within-host virus modeling frameworks do not explicitly include a human leukocyte antigen (HLA)-aware CTL response. Results We extended our previously published within-host sequence evolution simulator, wavess, to include an explicit CTL response, and share a method for identifying HLA-specific CTL epitopes given a founder virus sequence. We also updated the model to permit a variable recombination rate, which allows for modeling non-adjacent genes, segmented genomes, and recombination hotspots. These extensions to wavess allow for more accurate simulation of viruses and virus genes, particularly in regions of the genome where the immune response is dominated by CTLs (rather than antibodies). It also provides the foundation for investigations of how these newly-added biological mechanisms influence within-host evolution. Availability and implementation The core of wavess is written in Python 3, with helper functions written in R. It is available at https://github.com/MolEvolEpid/wavess.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Graph neural network for neutrino physics event reconstruction

Liquid argon time projection chamber (LArTPC) detector technology offers a wealth of high-resolution information on particle interactions, and leveraging that information to its full potential requires sophisticated automated reconstruction techniques. Here, this article describes NUGRAPH 2, a graph neural network for low-level reconstruction of simulated neutrino interactions in a LArTPC detector. Simulated neutrino interactions in the MicroBooNE detector geometry are described as heterogeneous graphs, with energy depositions on each detector plane forming nodes on planar subgraphs. The network utilizes a multihead attention message-passing mechanism to perform background filtering and semantic labeling on these graph nodes, identifying those associated with the primary physics interaction with 98.0% efficiency and labeling them according to particle type with 94.9% efficiency. The network operates directly on detector observables across multiple two-dimensional representations but utilizes a three-dimensional-context-aware mechanism to encourage consistency between these representations. Model inference takes 0.12 s / event on a CPU and 0.005 s / event batched on a GPU. This architecture is designed to be a general-purpose solution for particle reconstruction in neutrino physics, with the potential for deployment across a broad range of detector technologies, and offers a core convolution engine that can be leveraged for a variety of tasks beyond the two described in this paper.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

BraggNN : fast X-ray Bragg peak analysis using deep learning

X-ray diffraction based microscopy techniques such as high-energy diffraction microscopy (HEDM) rely on knowledge of the position of diffraction peaks with high precision. These positions are typically computed by fitting the observed intensities in detector data to a theoretical peak shape such as pseudo-Voigt. As experiments become more complex and detector technologies evolve, the computational cost of such peak-shape fitting becomes the biggest hurdle to the rapid analysis required for real-time feedback in experiments. To this end, we propose BraggNN, a deep-learning based method that can determine peak positions much more rapidly than conventional pseudo-Voigt peak fitting. When applied to a test dataset, peak center-of-mass positions obtained from BraggNN deviate less than 0.29 and 0.57 pixels for 75 and 95% of the peaks, respectively, from positions obtained using conventional pseudo-Voigt fitting (Euclidean distance). When applied to a real experimental dataset and using grain positions from near-field HEDM reconstruction as ground-truth, grain positions using BraggNN result in 15% smaller errors compared with those calculated using pseudo-Voigt. Recent advances in deep-learning method implementations and special-purpose model inference accelerators allow BraggNN to deliver enormous performance improvements relative to the conventional method, running, for example, more than 200 times faster on a consumer-class GPU card with out-of-the-box software.

36 MATERIALS SCIENCE↗