Search NASA⌕ Search

SEARCH · Search NASA

Results for “Model fitting”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

The luminosity and stellar mass functions of GRB host galaxies: Insight into the metallicity bias

Long-duration gamma-ray bursts (GRBs) are powerful probes of the star formation history of the universe, but the correlation between the two depends on the highly debated presence and strength of a metallicity bias. To investigate this correlation, we use a phenomenological model that successfully describes star formation rates, luminosities, and stellar masses of star-forming galaxies and apply it to GRB production. We predict the luminosities, stellar masses, and metallicities of host galaxies depending on the presence (or absence) of a metallicity bias. Our best-fitting model includes a moderate metallicity bias, broadly consistent with the large majority of the long-duration GRBs in metal-poor environments originating from a collapsar (probability ∼83%, with [0.74;0.91] range at 90% confidence level), but with a secondary contribution (∼17%) from a metal-independent production channel, such as binary evolution. Because of the mass–metallicity relation of galaxies, the maximum likelihood model predicts that the metal-independent channel becomes dominant at z≲2, where hosts have higher metallicities and collapsars are suppressed. This possibly explains why some studies find no clear evidence of a metal bias based on low-z samples. However, while metallicity predictions match observations well at high redshift (z≳2), there is tension with low-redshift observations, since a significant fraction of GRB hosts are predicted to have (near) solar metallicity. This is in contrast to observations, unless obscured, metal-rich hosts are preferentially missed in current data sets, and suggests that lower efficiencies of the metal-independent GRB channel might be preferred following a comprehensive fit that includes metallicity of GRB hosts from complete samples. Overall, we are able to clearly establish the presence of a metallicity bias for GRB production, but continued characterization of GRB host galaxies is needed to quantify its strength. Tabulated model predictions are available in electronic format.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Toward Accurate Modeling of Galaxy Clustering on Small Scales: Constraining the Galaxy-halo Connection with Optimal Statistics

Applying halo models to analyze the small-scale clustering of galaxies is a proven method for characterizing the connection between galaxies and their host halos. Such works are often plagued by systematic errors or limited to clustering statistics that can be predicted analytically. In this work, we employ a numerical mock-based modeling procedure to examine the clustering of Sloan Digital Sky Survey DR7 galaxies. We apply a standard halo occupation distribution (HOD) model to dark matter only simulations with a ΛCDM cosmology. To constrain the theoreStical models, we utilize a combination of galaxy number density and selected scales of the projected correlation function, redshift-space correlation function, group multiplicity function, average group velocity dispersion, mark correlation function, and counts-in-cells statistics. We design an algorithm to choose an optimal combination of measurements that yields tight and accurate constraints on our model parameters. Compared to previous work using fewer clustering statistics, we find a significant improvement in the constraints on all parameters of our halo model for two different luminosity-threshold galaxy samples. Most interestingly, we obtain unprecedented high-precision constraints on the scatter in the relationship between galaxy luminosity and halo mass. However, our best-fit model results in significant tension (>4σ) for both samples, indicating the need to add second-order features to the standard HOD model. To guarantee the robustness of these results, we perform an extensive analysis of the systematic and statistical errors in our modeling procedure, including a first of its kind study of the sensitivity of our constraints to changes in the halo mass function due to baryonic physics.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Predictive models of radiative neutrino masses

We discuss two models of radiative neutrino mass generation. The first model features one–loop Zee model with Z{sub 4} symmetry. The second model is the two–loop neutrino mass model with singly- and doubly-charged scalars. These two models fit neutrino oscillation data well and predict some interesting rates for lepton flavor violation processes.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

CME PROPAGATION: WHERE DOES AERODYNAMIC DRAG “TAKE OVER”?

We investigate the Sun–Earth dynamics of a set of eight well observed solar coronal mass ejections (CMEs) using data from the Solar Terrestrial Relations Observatory spacecraft. We seek to quantify the extent to which momentum coupling between these CMEs and the ambient solar wind (i.e., the aerodynamic drag) influences their dynamics. To this end, we use results from a 3D flux rope model fit to the CME data. We find that solar wind aerodynamic drag adequately accounts for the dynamics of the fastest CME in our sample. For the relatively slower CMEs, we find that drag-based models initiated below heliocentric distances ranging from 15 to 50 R{sub ⊙} cannot account for the observed CME trajectories. This is at variance with the general perception that the dynamics of slow CMEs are influenced primarily by solar wind drag from a few R{sub ⊙} onwards. Several slow CMEs propagate at roughly constant speeds above 15–50 R{sub ⊙}. Drag-based models initiated above these heights therefore require negligible aerodynamic drag to explain their observed trajectories.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Atacama Cosmology Telescope: Multiprobe cosmology with unWISE galaxies and ACT DR6 CMB lensing

We present a joint analysis of the cosmic microwave background (CMB) lensing power spectra measured from the Data Release 6 of the Atacama Cosmology Telescope (ACT) and Planck PR4, cross-correlations between the ACT and Planck lensing reconstruction and galaxy clustering from unWISE, and the unWISE clustering auto-spectrum. We obtain 1.5% constraints on the matter density fluctuations at late times parametrized by the best constrained parameter combination 𝑆$^{3⁢x⁢2⁢pt}_{8}$ ≡ 𝜎 8 ⁢(Ω 𝑚 /0.3) 0.4 = 0.815 ± 0.012. The commonly used 𝑆 8 ≡ 𝜎 8⁢ (Ω 𝑚 /0.3) 0.5 parameter is constrained to 𝑆 8 = 0.816 ± 0.015. In combination with baryon acoustic oscillation (BAO) measurements we find 𝜎 8 = 0.815 ± 0.012. We also present sound-horizon-independent estimates of the present day Hubble rate of 𝐻 0 = 66.4$^{+3.2}_{−3.7}$ km s −1 Mpc −1 from our large scale structure data alone and 𝐻 0 = 64.3$^{+2.1}_{−2.4}$ km s −1 Mpc −1 in combination with uncalibrated supernovae from Pantheon+. Using parametric estimates of the evolution of matter density fluctuations, we place constraints on cosmic structure in a range of high redshifts typically inaccessible with cross-correlation analyses. Combining lensing cross- and autocorrelations, we derive a 3.3% constraint on the integrated matter density fluctuations above 𝑧 = 2.4, one of the tightest constraints in this redshift range and fully consistent with a Λ cold dark matter (Λ⁢CDM) model fit to the primary CMB from Planck. Finally, combining with primary CMB observations and using the extended low redshift coverage of these combined datasets we derive constraints on a variety of extensions to the Λ⁢CDM model including massive neutrinos, spatial curvature, and dark energy. We find in flat Λ⁢CDM⁢ ∑𝑚 𝜈 < 0.12 eV at 95% confidence using the large scale structure data, BAO measurements from Sloan Digital Sky Survey, and primary CMB observations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Battery Life Prediction Using Reduced-Order Physics Models and Machine Learning (CRADA Final Report)

Phase 1 (Original CRADA, plus no-cost extension modifications #1-3, 6/1/2017 to 3/13/2021): The Australian Department of Defence (AUDoD) is performing accelerated aging tests of Li-ion batteries to benchmark their reliability and degradation characteristics. Using its previously developed battery lifetime predictive model framework, the National Laboratory of the Rockies (NLR) will develop analytical models based the AUDoD data to predict lifetime of the multiple Li-ion battery chemistries under real-world use scenarios of interest to AUDoD. The NLR model is based on physical degradation mechanisms encountered by Li-ion batteries and has been previously validated. Phase 2 (CRADA modification #4, plus no-cost extension modification #5, 2/22/2021 to 3/30/2025): Train and support Australian Department of Defence personnel to use NLR software for model-based estimation of Li-ion battery lifetime using accelerated battery aging data collected by the Australian Department of Defence. Under separate DOE funding from 2019 to 2021, NLR enhanced its battery life-prediction software using machine learning algorithms to automate portions of the model-fitting process, requiring significantly less labor and expert judgment and also adding uncertainty quantification, increasing statistical rigor. Under Phase 2, NLR will customize NLR Software and provide it to AuDoD. NLR will enhance its NLR Model to capture aging modes of AuDoD's multi-cell modules, including cell-balancing effects. NLR will develop example single-cell and multi-cell models based on one AuDoD battery aging dataset. NLR will train AuDoD personnel on NLR Software. By the conclusion of the project, NLR will have provided AuDoD the training materials, a user manual and software needed to perform their own analysis of additional and/or future battery aging datasets.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Modeling the Spectral Energy Distribution of the Active Galactic Nucleus inside NGC 4395

We study the broadband spectral energy distribution (SED) of the prototypical low-mass active galactic nucleus (AGN) in NGC 4395. We jointly model the optical through mid-IR SED with a combination of galaxy and AGN light, and find that on arcsecond scales the AGN dominates at most wavelengths. However, there is still some ambiguity about emission from the galaxy, owing partially to the strong short-term variability of the black hole. We investigate the use of smooth- and clumpy-torus models in order to disentangle the nuclear IR emission, as well as exploring the use of poloidal wind emission to account for the blue spectral slope observed in the near-IR. Even when simultaneously fitting the full optical–IR spectral range, we find that degeneracies still remain in the best-fit models. We conclude that high-spatial-resolution and wider-wavelength coverage with the JWST is needed to understand the mid-IR emission in this complex, highly variable object, which is the best nearby example to provide a blueprint to finding other low-mass AGNs via their mid-IR emission in the future.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Neural Posterior Estimation for Scalable and Accurate Inverse Parameter Inference in Li-Ion Batteries

Diagnosing the internal state of Li-ion batteries is critical for battery research, operation of real-world systems, and prognostic evaluation of remaining lifetime. By using physics-based models to perform probabilistic parameter estimation via Bayesian calibration, diagnostics can account for the uncertainty due to model fitness, data noise, and the observability of any given parameter. However, Bayesian calibration in Li-ion batteries using electrochemical data is computationally intensive even when using a fast surrogate in place of physics-based models, requiring many thousands of model evaluations. A fully amortized alternative is neural posterior estimation (NPE). NPE shifts the computational burden from the parameter estimation step to data generation and model training, reducing the parameter estimation time from minutes to milliseconds, enabling real-time applications. The present work shows that NPE can infer parameters equally or more accurately than Bayesian calibration, even if it leads to higher voltage reconstruction errors. We also demonstrate that the higher computational costs for data generation are tractable even in high-dimensional cases (ranging from 6 to 27 estimated parameters). The NPE method also offers several interpretability advantages over Bayesian calibration, such as local parameter sensitivity to specific regions of the voltage curve. The NPE method is demonstrated using an experimental fast charge dataset, with parameter estimates validated against measurements of loss of lithium inventory and loss of active material. The implementation is made available in a companion repository (https://github.com/NatLabRockies/BatFIT).

25 ENERGY STORAGE↗
DOE OSTI2024

Kinetic assessment of pulp mill-derived lime mud calcination in high CO 2 atmosphere

The chemical pulping of biomass involves the recycling of calcium through the calcination of lime mud, which is mostly comprised of calcium carbonate (CaCO 3 ). Lime mud decomposes under elevated temperatures to generate calcium oxide (CaO) and carbon dioxide (CO 2 ), the kinetics of which are strongly influenced by the CO 2 partial pressure and temperature. Oxy-fuel combustion and electrified lime kilns for lime mud calcination are intriguing methods to decarbonize this highly polluting operation within the biomass pulping industry. However, the high CO 2 concentration in oxy-fuel and electrified calcination processes alters the kinetics and overall reactivity of lime mud. For the first time, a model-fitting method is used to determine the kinetic parameters for lime mud calcination under a wide range of temperatures (550 °C–1250 °C) and under different concentrations of CO2 (0 %, 15 %, 50 %, and 90 %). A kinetic model is developed that accurately predicts the reaction rates as a function of temperature and CO 2 concentration. The apparent activation of energy for lime mud calcination is elevated under a high CO 2 environment. Relative to inert gas (N 2 , Ar), the temperature window for calcination is much smaller under high CO 2 environments. The presence of Na in lime mud does not seem to affect calcination under a high CO 2 environment. Finally, particle size variation does not have a significant effect on calcination under a high CO 2 environment.

09 BIOMASS FUELS↗
DOE OSTI2017

NEOWISE Reactivation Mission Year Three: Asteroid Diameters and Albedos

The Near-Earth Object Wide-field Infrared Survey Explorer (NEOWISE) reactivation mission has completed its third year of surveying the sky in the thermal infrared for near-Earth asteroids and comets. NEOWISE collects simultaneous observations at 3.4 and 4.6 μ m of solar system objects passing through its field of regard. These data allow for the determination of total thermal emission from bodies in the inner solar system, and thus the sizes of these objects. In this paper, we present thermal model fits of asteroid diameters for 170 NEOs and 6110 Main Belt asteroids (MBAs) detected during the third year of the survey, as well as the associated optical geometric albedos. We compare our results with previous thermal model results from NEOWISE for overlapping sample sets, as well as diameters determined through other independent methods, and find that our diameter measurements for NEOs agree to within 26% (1 σ ) of previously measured values. Diameters for the MBAs are within 17% (1 σ ). This brings the total number of unique near-Earth objects characterized by the NEOWISE survey to 541, surpassing the number observed during the fully cryogenic mission in 2010.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Comparison of simulated neutrino emission models with data on Supernova 1987A

Here we compare models of supernova (SN) neutrino emission with the Kamiokande II data on SN 1987A using the Bayesian approach. These models are taken from simulations and are representative of current one-dimensional SN models. We find that models with a brief accretion phase of neutrino emission are the most favored. This result is not affected by varying the overall flux normalization or considering neutrino oscillations. We also check the compatibility of the best-fit models with the data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Single-crystal elasticity of (Al,Fe)-bearing bridgmanite up to 82 GPa

Abstract Thermoelastic properties of mantle candidate minerals are essential to our understanding of geophysical phenomena, geochemistry, and geodynamic evolutions of the silicate Earth. However, the lower-mantle mineralogy remains much debated due to the lack of single-crystal elastic moduli (Cij) and aggregate sound velocities of (Al,Fe)-bearing bridgmanite, the most abundant mineral of the planet, at the lower mantle pressure-temperature (P-T) conditions. Here we report single-crystal Cij of (Al,Fe)-bearing bridgmanite, Mg0.88Fe0.1Al0.14Si0.90O3 (Fe10-Al14-Bgm) with Fe3+/ΣFe = ~0.65, up to ~82 GPa using X-ray diffraction (XRD), Brillouin light scattering (BLS), and impulsive stimulated light scattering (ISLS) measurements in diamond-anvil cells (DACs). Two crystal platelets with orientations of (–0.50, 0.05, –0.86) and (0.65, –0.59, 0.48), that are sensitive to deriving all nine Cij, are used for compressional and shear wave velocity (νP and νS) measurements as a function of azimuthal angles over 200° at each experimental pressure. Our results show that all Cij of singe-crystal Fe10-Al14-Bgm increase monotonically with pressure with small uncertainties of 1–2% (±1σ), except C55 and C23, which have uncertainties of 3–4%. Using the third-order Eulerian finite-strain equations to model the elasticity data yields the aggregate adiabatic bulk and shear moduli and respective pressure derivatives at the reference pressure of 25 GPa: KS = 326 ± 4 GPa, µ = 211 ± 2 GPa, KS′ = 3.32 ± 0.04, and µ′ = 1.66 ± 0.02 GPa. The high-pressure aggregate νS and νP of Fe10-Al14-Bgm are 2.6–3.5% and 3.1–4.7% lower than those of MgSiO3 bridgmanite end-member, respectively. These data are used with literature reports on bridgmanite with different Fe and Al contents to quantitatively evaluate pressure and compositional effects on their elastic properties. Comparing with one-dimensional seismic profiles, our modeled velocity profiles of major lower-mantle mineral assemblages at relevant P-T suggest that the lower mantle could likely consist of about 89 vol% (Al,Fe)-bearing bridgmanite. After considering uncertainties, our best-fit model is still indistinguishable from pyrolitic or chondritic models.

Geochemistry & Geophysics↗
DOE OSTI2022

Preliminary Results for Using Uncertainty and Out-of-distribution Detection to Identify Unreliable Predictions.

As machine learning (ML) models are deployed into an ever-diversifying set of application spaces, ranging from self-driving cars to cybersecurity to climate modeling, the need to carefully evaluate model credibility becomes increasingly important. Uncertainty quantification (UQ) provides important information about the ability of a learned model to make sound predictions, often with respect to individual test cases. However, most UQ methods for ML are themselves data-driven and therefore susceptible to the same knowledge gaps as the models themselves. Specifically, UQ helps to identify points near decision boundaries where the models fit the data poorly, yet predictions can score as certain for points that are under-represented by the training data and thus out-of-distribution (OOD). One method for evaluating the quality of both ML models and their associated uncertainty estimates is out-of-distribution detection (OODD). We combine OODD with UQ to provide insights into the reliability of the individual predictions made by an ML model.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Quantifying mean, variability, and uncertainty in indoor radon exposure in Pennsylvania using random forest and quantile regression forest models

Radon is a naturally occurring radioactive gas that poses a serious health risk as the primary cause of lung cancer in non-smokers. Despite the well-known adverse association with health outcomes, current radon exposure assessments are limited to county-level or average-level estimates, which fail to capture regional variability. This study uses Machine Learning models, including Random Forest (RF) and Quantile Regression Forest (QRF), to estimate the indoor radon concentrations at the ZCTA (Zip code tabulation area)-level and characterize uncertainties in model estimates. Incorporating geological, meteorological, and building-specific data, the models aim to improve radon risk assessment by capturing mean exposure, variability, and extreme concentration levels. Processed radon test data (n = 718,111) were analyzed using average, variability, and quantile prediction methods. Models that estimate the average radon exposure at the ZCTA-level can yield promising model-fit results, but they do not capture the underlying variability of indoor radon exposure within a ZCTA. We utilize volatility analyses to identify characteristics indicative of high variability of indoor radon exposure. We also show that a QRF model can be used to estimate upper quantiles of residential radon exposure, thereby uncovering localized areas of elevated exposure that were not apparent in mean estimates. The results highlighted the need for a deep characterization of exposure risk and show that regions with moderate average exposure levels could still harbor extreme outliers with implications for evaluating health risks. Utilizing multiple radon exposure models allows for a deeper characterization of radon risk within a geographic area and can better identify high-risk areas. The results from this study provide a foundation for developing mitigation strategies and examining associations between radon exposure and health outcomes at fine scales. Future research should extend the geographic scope and incorporate additional environmental risk factors to establish a comprehensive framework for risk assessment.

Lee, Heechan [ORNL]↗
DOE OSTI2015

The AU MIC debris disk: Far-infrared and submillimeter resolved imaging

We present far-infrared and submillimeter maps from the Herschel Space Observatory and the James Clerk Maxwell Telescope of the debris disk host star AU Microscopii. Disk emission is detected at 70, 160, 250, 350, 450, 500, and 850 μm. The disk is resolved at 70, 160, and 450 μm. In addition to the planetesimal belt, we detect thermal emission from AU Mic’s halo for the first time. In contrast to the scattered light images, no asymmetries are evident in the disk. The fractional luminosity of the disk is 3.9×10{sup −4} and its milimeter-grain dust mass is 0.01 M{sub ⊕} (±20%). We create a simple spatial model that reconciles the disk spectral energy distribution as a blackbody of 53 ± 2 K (a composite of 39 and 50 K components) and the presence of small (non-blackbody) grains which populate the extended halo. The best-fit model is consistent with the “birth ring” model explored in earlier works, i.e., an edge-on dust belt extending from 8.8 to 40 AU, but with an additional halo component with an r{sup −1.5} surface density profile extending to the limits of sensitivity (140 AU). We confirm that AU Mic does not exert enough radiation force to blow out grains. For stellar mass-loss rates of 10–100 times solar, compact (zero porosity) grains can only be removed if they are very small; consistently with previous work, if the porosity is 0.9, then grains approaching 0.1 μm can be removed via corpuscular forces (i.e., the stellar wind).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

BatteryPro: A Python Toolkit for Battery Data Analysis and Machine Learning Predictions

Analyzing battery test data for research & development can be time-consuming since battery tests often run on the order of months to years, generating large volumes of data. BatteryPro is a comprehensive Python package and software designed to facilitate advanced analysis and performance predictions for battery test data. Developed for battery researchers, it supports data types from widely used battery testing instruments, including MACCOR and Biologic cycling systems. The software provides a variety of tools for extracting and plotting key battery parameters such as time, voltage, capacity, current, and pressure. In addition to its extensive data analysis capabilities, BatteryPro features a dedicated machine learning module that employs a Bayesian Gaussian Mixture Model (GMM) to predict battery performance and degradation. Users can generate synthetic capacity fade data, calculate fade metrics, and leverage predictive models to forecast long-term battery behavior. The software's graphical user interface (GUI) enhances usability, allowing researchers to upload, merge, and analyze multiple data files with full customizability. The GUI also supports machine learning predictions, enabling users to fit models and make predictions based on selected data and parameters. BatteryPro is built using QtDesigner, scikit-learn, matplotlib, and pandas, ensuring a high level of customization, flexibility, and accuracy in battery data analysis. This tool aims to empower researchers with the ability to perform detailed battery analysis and make informed predictions, ultimately advancing the field of battery research.

25 - ENERGY STORAGE↗
DOE OSTI2016

Parameterization of photon beam dosimetry for a linear accelerator

Purpose: In radiation therapy, accurate data acquisition of photon beam dosimetric quantities is important for (1) beam modeling data input into a treatment planning system (TPS), (2) comparing measured and TPS modeled data, (3) the quality assurance process of a linear accelerator’s (Linac) beam characteristics, (4) the establishment of a standard data set for comparison with other data, etcetera. Parameterization of the photon beam dosimetry creates a data set that is portable and easy to implement for different applications such as those previously mentioned. The aim of this study is to develop methods to parameterize photon beam dosimetric quantities, including percentage depth doses (PDDs), profiles, and total scatter output factors (S{sub cp}). Methods: S{sub cp}, PDDs, and profiles for different field sizes, depths, and energies were measured for a Linac using a cylindrical 3D water scanning system. All data were smoothed for the analysis and profile data were also centered, symmetrized, and geometrically scaled. The S{sub cp} data were analyzed using an exponential function. The inverse square factor was removed from the PDD data before modeling and the data were subsequently analyzed using exponential functions. For profile modeling, one halfside of the profile was divided into three regions described by exponential, sigmoid, and Gaussian equations. All of the analytical functions are field size, energy, depth, and, in the case of profiles, scan direction specific. The model’s parameters were determined using the minimal amount of measured data necessary. The model’s accuracy was evaluated via the calculation of absolute differences between the measured (processed) and calculated data in low gradient regions and distance-to-agreement analysis in high gradient regions. Finally, the results of dosimetric quantities obtained by the fitted models for a different machine were also assessed. Results: All of the differences in the PDDs’ buildup and the profiles’ penumbra regions were less than 2 and 0.5 mm, respectively. The differences in the low gradient regions were 0.20% ± 0.20% (<1% for all) and 0.50% ± 0.35% (<1% for all) for PDDs and profiles, respectively. For S{sub cp} data, all of the absolute differences were less than 0.5%. Conclusions: This novel analytical model with minimum measurement requirements was proved to accurately calculate PDDs, profiles, and S{sub cp} for different field sizes, depths, and energies.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2019

Prediction Uncertainties beyond the Range of Experience: A Case Study in Inertial Confinement Fusion Implosion Experiments

Scientists often predict physical outcomes, e.g., experimental results, with the assistance of computer codes that, at their best, only coarsely approximate reality. Coarse predictions are challenging in critical part due to the multitude of seemingly arbitrary yet consequential decisions that must be made such as choice of relevant data, calibration of code parameters, and construction of empirical discrepancy forms. In this paper, we present a case study in the context of inertial confinement fusion (ICF) implosion experiments where extrapolative predictions are needed with quantified uncertainties. The purpose of this case study is to reflect relevant statistical methods, as applied to ICF model fitting and prediction, to document the numerous decisions that must be made in the prediction pipeline, to extend a complex example in extrapolation to the uncertainty quantification (UQ) community, and to reflect on the challenges we encountered supporting extrapolations with imperfect models and thereby recommend several future research directions. We conclude with a discussion about the UQ community's role in less than ideal predictive scenarios like our ICF exercise.

97 MATHEMATICS AND COMPUTING↗