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All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Development and Application of a Tool for Optimizing Composite Matrix Viscoplastic Material Parameters

This document describes a recently developed analysis tool that enhances the resident capabilities of the Micromechanics Analysis Code with the Generalized Method of Cells (MAC/GMC) and its application. MAC/GMC is a composite material and laminate analysis software package developed at NASA Glenn Research Center. The primary focus of the current effort is to provide a graphical user interface (GUI) capability that helps users optimize highly nonlinear viscoplastic constitutive law parameters by fitting experimentally observed/measured stress-strain responses under various thermo-mechanical conditions for braided composites. The tool has been developed utilizing the MATrix LABoratory (MATLAB) (The Mathworks, Inc., Natick, MA) programming language. Illustrative examples shown are for a specific braided composite system wherein the matrix viscoplastic behavior is represented by a constitutive law described by seven parameters. The tool is general enough to fit any number of experimentally observed stress-strain responses of the material. The number of parameters to be optimized, as well as the importance given to each stress-strain response, are user choice. Three different optimization algorithms are included: (1) Optimization based on gradient method, (2) Genetic algorithm (GA) based optimization and (3) Particle Swarm Optimization (PSO). The user can mix and match the three algorithms. For example, one can start optimization with either 2 or 3 and then use the optimized solution to further fine tune with approach 1. The secondary focus of this paper is to demonstrate the application of this tool to optimize/calibrate parameters for a nonlinear viscoplastic matrix to predict stress-strain curves (for constituent and composite levels) at different rates, temperatures and/or loading conditions utilizing the Generalized Method of Cells. After preliminary validation of the tool through comparison with experimental results, a detailed virtual parametric study is presented wherein the combined effects of temperature and loading rate on the predicted response of a braided composite is investigated.

thermo-mechanical; MATLAB; GUI-Based Too↗
NASA NTRS2017

Stellar and Planetary Parameters for K2's Late-Type Dwarf Systems from C1 to C5

The NASA K2 (Kepler-2) mission uses photometry to find planets transiting stars of various types. M dwarfs are of high interest since they host more short-period planets than any other type of main-sequence star and transiting planets around M dwarfs have deeper transits compared to other main-sequence stars. In this paper, we present stellar parameters from K and M dwarfs hosting transiting planet candidates discovered by our team. Using the SOFI (Son OF Isaac - ESA's earlier, similar instrument) spectrograph on the European Southern Observatory's New Technology Telescope, we obtained R approximately equal to 1000 J-, H-, and K-band (0.95-2.52 micron) spectra of 34 late-type K2 planet and candidate planet host systems and 12 bright K4-M5 dwarfs with interferometrically measured radii and effective temperatures. Out of our 34 late-type K2 targets, we identify 27 of these stars as M dwarfs. We measure equivalent widths of spectral features, derive calibration relations using stars with interferometric measurements, and estimate stellar radii, effective temperatures, masses, and luminosities for the K2 planet hosts. Our calibrations provide radii and temperatures with median uncertainties of 0.059 solar radii (16.09 percent) and 160 degrees Kelvin (4.33 percent), respectively. We then reassess the radii and equilibrium temperatures of known and candidate planets based on our spectroscopically derived stellar parameters. Since a planet's radius and equilibrium temperature depend on the parameters of its host star, our study provides more precise planetary parameters for planets and candidates orbiting late-type stars observed with K2. We find a median planet radius and an equilibrium temperature of approximately 3 solar radii and 500 degrees Kelvin, respectively, with several systems (K2-18b and K2-72e) receiving near-Earth-like levels of incident irradiation.

Martinez, Arturo O.↗
NASA NTRS2018

Vertical Profile of Cloud Optical Parameters Derived from Airborne Measurements Above, Inside and Below Clouds

Past strategies for retrieving cloud optical properties from remote sensing assumed significant limits for desired parameters such as semi-infinite optical thickness, single scattering albedo equaling unity (non-absorbing scattering), absence of spectral dependence of the optical thickness, etc., and only one optical parameter could be retrieved (either optical thickness or single scattering albedo). Here, we demonstrate a new method based on asymptotic theory for thick atmospheres, and the presence of a diffusion domain within the clouds that does not put restrictions and makes it possible to get two or even three optical parameters (optical thickness, single scattering albedo and phase function asymmetry parameter) for every wavelength independently. We applied this method to measurements of angular distribution of solar radiation above, inside and below clouds, obtained with NASA's Cloud Absorption Radiometer (CAR) over two cases of marine stratocumulus clouds; first case, offshore of Namibia and the second case, offshore of California. The observational and retrieval errors are accounted for by regularization, which allows stable and smooth solutions. Results show good potential for parameterization of the shortwave radiative properties (reflection, transmission, radiative divergence and heating rate) of water clouds.

airborne observations↗
NASA NTRS2018

The Statistical Properties of Solar Wind Temperature Parameters Near 1 Au

We present a long-duration (approx. 10 yr) statistical analysis of the temperatures, plasma betas, and temperature ratios for the electron, proton, and alpha-particle populations observed by the Wind spacecraft near 1 au. The mean(median) scalar temperatures are T(sub e)(sub tot) = 12.2(11.9) eV, T(sub p)(sub tot) = 12.7(8.6) eV, and T(sub a)(sub tot) = 23.9(10.8) eV. The mean(median) total plasma betas are Beta(sub e)(sub tot) = 2.31(1.09), Beta(sub p)(sub tot) = 1.79(1.05), and Beta(sub a)(sub tot) = 0.17(0.05). The mean(median) temperature ratios are (T(sub e)/T(sub p))(sub tot) = 1.64(1.27), (T(sub e)/T(sub a))(sub tot) = 1.24(0.82), and (T(sub a)/T(sub p))(sub tot) = 2.50(1.94). We also examined these parameters during time intervals that exclude interplanetary (IP) shocks, times within the magnetic obstacles (MOs) of interplanetary coronal mass ejections (ICMEs), and times that exclude MOs. The only times that show significant alterations to any of the parameters examined are those during MOs. In fact, the only parameter that does not show a significant change during MOs is the electron temperature. Although each parameter shows a broad range of values, the vast majority are near the median. We also compute particle–particle collision rates and compare to effective wave–particle collision rates. We find that, for reasonable assumptions of wave amplitude and occurrence rates, the effect of wave–particle interactions on the plasma is equal to or greater than the effect of Coulomb collisions. Thus, wave–particle interactions should not be neglected when modeling the solar wind.

Wilson, Lynn B., III↗
NASA NTRS2019

Parameters Inference and Model Reduction for the Single-Particle Model of Li Ion Cells

The Single-Particle Model (SPM) of Li ion cell is a computationally efficient model for simulating Li ion cell for weak to moderate currents. The model depends n a number of parameters describing the geometry and material properties of a cell components. In order to apply the model to simulating a cell, the best-fit parametric values have to be inferred from a constant discharge data. We report our efforts to determine the best-fit set for 18650 LP batteries. We found that rather than being best-fit by a particular point in the parametric space the data is fit equally well by an ensemble of points clustering about an effective multidimensional manifold in the parametric space. This property of the SPM is known to be shared by a multitude of the so-called "sloppy models" of complex systems, characterized by a few stiff directions in the parametric space, in which the predicted behavior varies significantly, and a number of sloppy directions in which the behavior doesn't change appreciably. Only the stiff parameters combinations are identifiable. Geometrical features of the BFM give insights to possible reduction of the SPM to a model having fewer sloppy parameters. We have constructed a hierarchy of such models. The fully reduced model depends on only stiff effective parameters which are identifiable and can be used for characterization of the battery's state of health.

Khasin, Michael↗
NASA NTRS2018

Comparison of CFD and DSMC Using Calibrated Transport Parameters

Hypersonic re-entry flows span a wide range of length scales where regions of both rarefied and continuum flow exist. Traditional computational fluid dynamics (CFD) techniques do not provide an accurate solution for the rarefied regions of such ‘mixed’ flow fields. Although direct simulation Monte Carlo (DSMC) can be used to accurately capture both the continuum and rarefied features of ‘mixed’ flow fields, they are computationally expensive when employed to simulate the low Knudsen number continuum regimes. Thus, a hybrid framework for seamlessly combining the two methodologies, CFD and DSMC, continues to be a topic of significant research effort. Ensuring consistency in the reaction kinetics and transport models employed within CFD and DSMC is a crucial requirement for obtaining a reliable solution from a hybrid framework for combined continuum/rarefied high speed flows. This paper represents one of the first studies to utilize the calibrated transport parameters developed to ensure consistency between CFD and DSMC solvers. The new variable soft sphere (VSS) parameters are compared to both previous “standard” variable hard sphere (VHS) parameters and also to solutions from the CFD transport properties that the new parameters were developed to reproduce.

Liechty, D. S.↗
NASA NTRS2020

Characteristics of DSD Bulk Parameters: Implication to Radar Rain Retrieval

With the use of 213,456 one-minute measured data of droplet-size distribution (DSD) of rain collected during several National Aeronautics and Space Administration (NASA)-sponsored field campaigns, the relationships between rainfall rate R, mass-weighted diameter D(m) and normalized intercept parameter N(w) of the gamma DSD are studied. It is found, based on the simulations of the gamma DSD model, that R, D(m) and N(w) are closely interrelated, and that the ratio of R to N(w) is solely a function of D(m), independent of the shape factor μ of the gamma distribution. Furthermore, the model-produced ratio agrees well with those from the DSD data. When a power-law equation is applied to fit the model data, we have: R=aN(w)D(sup b, sub m) , where a=1.588×10(exp −4) , b=4.706 . Analysis of two-parameter relationships such as R–D(m), N(w)–R and N(w)–D(m) reveals that R and D(m) are moderately correlated while N(w) and D(m) are negatively correlated. N(w) and R, however, are uncorrelated. The gamma DSD model also reveals that variation of R–D(m) relation is caused primarily by N(w). For the application of the Ku- and Ka-band dual-frequency radar for the retrieval of the DSD bulk parameters as well as the specific radar attenuations, the study is carried out to relate the dual-frequency radar reflectivity factors to the DSD and attenuation parameters.

Liang Liao↗
NASA NTRSDate not supplied

Early Information Parameter-Set Analysis for Satellite Close Approaches using Machine Learning

Understanding orbital mechanics is essential in space flight and navigation applications, and leveraging modern force models for flight path projection remains an important aspect in space mission design and operation. However, force models do not capture all the dynamics or perturbations in the space environment and thus are subject to errors in predicting the state vectors. The further out the predicted miss distance between spacecraft is from the time of closest approach (TCA), the larger the propagated errors in the predicted miss distance at TCA is. The dependency on these force models for spacecraft flight state prediction calls for a more reliable method that can quantify, or even reduce, these propagated errors. With recent advances in the field artificial intelligence, specifically in machine and deep learning algorithms, a model that implements these approaches can improve on the modern force model approach. The goal for this work is to provide an early-information decision-making threshold, in order to prioritize risk assessment implementation, given the ongoing increase of space objects. In analyzing the relationship of several parameters from conjunction data messages(CDMs) and solar information, early information becomes viable in miss distance prediction with unsupervised learning techniques, which learn the parameters that are linked together with miss distance and probability of collision (Pc) variables. Another approach implemented for identifying relationships within CDMs is supervised learning, in which a shallow neural network binary classifier learns to distinguish events with Pc values¡108. These parameters detected in the unsupervised process are then applied to a regression neural network, which predicts the miss distance at TCA for a specific event within a given uncertainty bound. For the regression neural network, a Long Short Term Memory (LSTM) neural network is implemented, which yields memory about each time step in an event. Using an LSTM network, the model learns to predict miss distance within 0.2km of the value measured at TCA. Although there is a limited amount of "close miss" data to train a network, the network learns to associate parameters, like large energy dissipation rates with the secondary object, with an elevated Pc

Brianna I. Robertson↗
NASA NTRS2023

Time Series of Magnetic Field Parameters Extracted from Merged Space-Weather MDI/HMI Active Region Patches as Potential Tool for Solar Flare Forecasting

Space-Weather MDI Active Region Patches (SMARPs) and Space-Weather HMI Active Region Patches (SHARPs) are two recently developed data products, which have been used for solar flare prediction studies. The present work is an effort to expand the application of SMARP and SHARP summary heliomagnetic parameters to the forecasting of solar flares. A new data product was derived by filtering, rescaling, and merging the SMARP and SHARP summary parameter data series, which were further converted into two-dimensional arrays by selecting time slices corresponding to R-value maxima, where R-value is a measure of the unsigned magnetic flux near polarity inversion lines. The resulting combined MDI-HMI time series currently span the period between April 4, 1996 and December 13, 2022, and can be extended to a more recent date, providing an opportunity to correlate and compare them with other solar activity parameters, such as the daily solar flare index, which is computed as a sum of the product of GOES X-ray flare magnitude and flare duration, for all M- and X-class flares during a day. Preliminary results demonstrate a significant overall correlation, with Pearson coefficients between 0.339 and 0.627. In addition, an oscillating pattern is seen in the daily-averaged sliding-window correlation coefficient. Time-lagged cross-correlation indicates that a leader-follower dynamic exists in some parameters, especially R-value, where they lead the flare index by at least several days, which may have potential for further application in space weather forecasting.

Heliophysics↗
NASA NTRSDate not supplied

4pPA1 - Turbulence Effects on Shaped Booms: Central Composite Design of Modeled Atmospheric Turbulence Parameters for Sonic Boom Propagation

Propagation of sonic booms through turbulence reduces mean sonic boom perception metric levels and also causes considerable variability. NASA’s PCBoom suite of sonic boom acoustic propagation modules includes an approximate method for accounting for the effects of turbulence on traditional N-wave sonic booms. The current implementation is ineffective for shaped sonic booms or low-booms, and it also has limited values for turbulence and ambient input parameters. NASA’s future X-59 low-boom community noise surveys require an accurate estimate of the effects of turbulence in regions across the USA, so the module must be improved. This work presents the methods of selecting which ambient and turbulence parameters should be included in an improved PCBoom turbulence module. Turbulence and ambient data were collected from two atmospheric model databases, the Climate Forecast System Version 2 and European Centre for Medium-Range Weather Forecast Reanalysis Version 5 (ERA5), hourly from 7 AM to 7 PM local time for 10 years at 19 locations across the USA. A fully-factorial propagation analysis using these parameters would be exceedingly computationally expensive. Instead, a central composite design was chosen resulting in 45 combinations of ambient and turbulence parameters. These 45 cases effectively sample the space balancing computational burden.

turbulence↗
NASA NTRSDate not supplied

Particle Size Distribution Parameters from SAGE III/ISS Extinction Spectra

Stratospheric aerosols play key roles in the chemistry and radiation balance of the atmosphere and are a key input parameter for global chemistry and climate models. The degree to which aerosols impact chemistry and radiation balance depends primarily on the relative abundance of different sized particles within the sample volume, often referred to as the particle size distribution (PSD). If the PSD is accurately known then other key modeling parameters (e.g., surface area density and effective radius) can be derived. Historically, occultation observations from orbital instruments such as SAGE III/ISS have been used to infer these PSD parameters by inverting the extinction coefficient spectra. However, past efforts routinely failed to account for measurement uncertainty and lacked a rigorous estimate of the inferred PSD uncertainty. We carried out a series of simulations to evaluate the accuracy of these inferences and, for every valid SAGE III/ISS extinction spectrum, determined the range of PSD parameters that fell withing the bounds of the extinction error bars. Special application of this method was applied to estimate the impact of the 2022 Hunga Tonga eruption had on particle size distributions.

Travis N Knepp↗
NASA NTRSDate not supplied

A Procedure for Obtaining Forecast Turbulence Parameters in the Atmospheric Boundary Layer for Acoustic Propagation

Propagation through turbulence causes a mean reduction in loudness of sonic booms and in some cases can cause significant fluctuations in loudness about that mean. Understanding these effects is important for accurately planning loudness levels during upcoming community noise tests with the X-59 aircraft as well as mitigating the risk for excessive loudness due to turbulence. Current methods for modeling acoustic propagation through turbulence require several atmospheric parameters including the atmospheric boundary layer (ABL) height, friction velocity, mixed-layer velocity scale, surface-layer temperature scale, as well as ambient pressure, temperature, and humidity within the ABL. Accurate forecasts of these parameters are needed for X-59 flight planning and may also be useful for planning future supersonic aircraft certification flight tests. This presentation showcases one method for obtaining the forecast turbulence and ambient atmospheric parameters from freely available Climate Forecast System Version 2 data. Forecast parameters are compared to measurements to assess accuracy and utility.

sonic boom↗
NASA NTRS2024

Southern California Water Resources: Using NASA Earth Observations to Monitor Seagrass Extent and Water Quality Parameters in Southern California

Eelgrass (Zostera marina) is a species of submerged aquatic vegetation found in shallow bays and estuaries with soft seafloors. Eelgrass is recognized for providing ecosystem benefits, such as carbon sequestration, sediment stabilization, water clarification, and fish and wildlife habitats. However, eelgrass is impacted by both marine and terrestrial threats associated with climate change. In this project, we worked with the Southern California Coastal Water Research Project, the National Oceanic and Atmospheric Administration’s National Marine Fisheries Service, and the State of California San Diego Regional Water Quality Control Board to investigate water quality parameters (i.e., chlorophyll-a concentration, sea surface temperature, and turbidity) associated with eelgrass in Newport Bay and Mission Bay, California. The team used Landsat 8 Operational Land Imager and Thermal Infrared Sensor, Landsat 9 Operational Land Imager-2 and Thermal Infrared Sensor-2, Sentinel-2 MultiSpectral Instrument, and ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station land surface temperature and cloud mask to create a time series of these water quality parameters from 2019–2023. We found that the parameters followed cyclical, seasonal patterns with turbidity and sea surface temperature peaking in the summer. We did not find that the parameters had changed significantly over longer time periods. These results will be used in a model developed by the Southern California Coastal Water Research Project to assess eelgrass ecosystem health and predict ecosystem occupancy in the future.

Katya Beener↗
NASA NTRSDate not supplied

Sensitivity Analysis for Takeoff and Landing Distance Parameters for Regional Air Mobility (RAM) Aircraft

Takeoff and landing performance models require low-speed aerodynamic and thrust characteristics that are difficult to predict in the conceptual design stage. Although an empirical approach can be utilized to predict takeoff and landing performance, this approach is inadequate for aircraft incorporating novel technologies and design such as those proposed for regional air mobility aircraft. The focus of this paper is to understand the sensitivity of takeoff and landing distance to aerodynamic, propulsive, and other parameters of interest for regional air mobility class vehicles. The aerodynamic and propulsive characteristics are generated and modified using simplified approximations based on fundamental physics and integrated into the equations of motion to calculate takeoff and landing distances. The most impactful parameters for takeoff and landing were those related to propulsion and the lift curve. Additional impactful parameters for landing included braking coefficient, approach angle, and wing incidence. Lesser impact was observed for the parameters describing the low-speed drag polar.

Gabino Martinez Rodriguez↗
NASA NTRS2023

Effects of Optimisation Parameters on Data-Driven Magnetofrictional Modelling of Active Regions

Context . The solar magnetic field plays an essential role in the formation, evolution, and dynamics of large-scale eruptive structures in the corona. The estimation of the coronal magnetic field, the ultimate driver of space weather, particularly in the ‘low’ and ‘middle’ corona, is presently limited due to practical difficulties. Data-driven time-dependent magnetofrictional modelling (TMFM) of active region magnetic fields has been proven to be a useful tool to study the corona. The input to the model is the photospheric electric field that is inverted from a time series of the photospheric magnetic field. Constraining the complete electric field, that is, including the non-inductive component, is critical for capturing the eruption dynamics. We present a detailed study of the effects of optimisation of the non-inductive electric field on the TMFM of AR 12473. Aims . We aim to study the effects of varying the non-inductive electric field on the data-driven coronal simulations, for two alternative parametrisations. By varying parameters controlling the strength of the non-inductive electric field, we wish to explore the changes in flux rope formation and their early evolution and other parameters, for instance, axial flux and magnetic field magnitude. Methods . We used the high temporal and spatial resolution cadence vector magnetograms from the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO). The non-inductive electric field component in the photosphere is critical for energising and introducing twist to the coronal magnetic field, thereby allowing unstable configurations to be formed. We estimated this component using an approach based on optimising the injection of magnetic energy. Results . Our data show that flux ropes are formed in all of the simulations except for those with the lower values of these optimised parameters. However, the flux rope formation, evolution and eruption time varies depending on the values of the optimisation parameters. The flux rope is formed and has overall similar evolution and properties with a large range of non-inductive electric fields needed to determine the non-inductive electric field component that is critical for energising and introducing twist to the coronal magnetic field. Conclusions . This study shows that irrespective of non-inductive electric field values, flux ropes are formed and erupted, which indicates that data-driven TMFM can be used to estimate flux rope properties early in their evolution without needing to employ a lengthy optimisation process.

A. Kumari↗
DOE OSTI2025

The relative importance of building design parameters in reducing energy use and sensible heat release from buildings in light of forecasted future weather data and building coverage ratio

Buildings typically have a 60-to-75-year lifespan before they require significant maintenance or modifications. However, most builders evaluate the performance of their new buildings using whole-building energy simulation tools based on the current typical meteorological year (TMY) file or actual meteorological year. The energy use consumption and sensible heat release pattern observed from buildings could potentially change based on shifting global climates. Therefore, the recommended energy-efficiency design parameters might also change during these periods. In this study, we evaluate the role of different building design parameters, such as material reflectivity, HVAC COP, and insulation values, on building energy usage and sensible heat release from buildings with different building coverage ratios (BCR), based on the current and future weather file TMY (fTMY) for the middle of the century (2040–2060). The role of sensible heat release from buildings is not accounted for accurately while estimating building energy usage in most whole-building energy simulations. The study conducts a series of whole-building energy simulation analyses using EnergyPlus to evaluate the role of different design parameters based on TMY and fTMY weather conditions. The analysis is conducted for two hot desert climatic cities: Phoenix (USA) and Abu Dhabi (UAE). The results show that, for the base case in a future climate, the sensible heat release is reduced by an average of 30% due to the reduced delta T between the surface and ambient air. Further, the results show an increase in total energy consumption by 5% annually. The results also show that, for buildings with traditional coatings, shorter buildings release more heat than taller buildings. On the other hand, for buildings with reflective paints, shorter buildings release less heat than taller buildings. The findings from this study can be used by policymakers, utility companies, and builders to better understand the relative role of different building design parameters while constructing new and retrofitting existing buildings.

Alhazmi, Mansour [King Fahd University of Petroleu↗
DOE OSTI2025

Use of Rig Parameter Data in Bit Constraint Models for Improved Drilling Performance at The Geysers

Surface parameter measurements are routinely used during deep well construction to monitor and guide drilling conditions for improved performance and reduced costs. However, these measurements are of reduced value without a standard to aid in evaluation and decision making. A method is demonstrated whereby drill bit constraint models are used to interpret drilling response parameters. Drill rig parameter data for well GDC-36 at the Geysers Geothermal Field Power were acquired by Geysers Power Company and drilling contractor Kenai Drilling using Pason US DataHub and evaluated. Drilling parameters are evaluated using laboratory-validated rock reduction models for predicting the phenomenological response of drag bits (Detournay and Defourny, 1992) along with other model constraints in computational algorithms. The method is used to evaluate overall bit performance, monitor bit integrity, and detect the presence of drillstring vibrations and other conditions contributing to bit failure; comparisons are made to observations of bit wear and damage. The method will be applied in real-time to improve decision-making on subsequent wells and has applicability to development of advanced analytics on future geothermal wells using real-time electronic drilling recorder (EDR) data for improved performance and reduced drilling costs.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

First Estimation of Model Parameters for Neutrino-Induced Nucleon Knockout Using Simulation-Based Inference

To enable an accurate determination of oscillation parameters, accelerator-based neutrino experiments require detailed simulations of nuclear interaction physics in the GeV regime. While substantial effort from both theory and experiment is currently being invested to improve the fidelity of these simulations, their present deficiencies typically oblige experimental collaborations to resort to empirical tuning of simulation model parameters. As the precision requirements of the field continue to become more stringent, machine learning techniques may provide a powerful means of handling corresponding growth in the complexity of future neutrino interaction model tuning exercises. To study the suitability of simulation-based inference (SBI) for this physics application, in this paper we revisit a tuned configuration of the GENIE neutrino event generator that was originally developed by the MicroBooNE collaboration. Despite closely reproducing the adopted values of four physics parameters when confronted with the tuned cross-section predictions as input, we find that our trained SBI algorithm prefers modestly different values (within MicroBooNE's assigned uncertainties) and achieves slightly better goodness-of-fit when inference is run on the experimental data set originally used by MicroBooNE. We also find that our trained algorithm can create a fair approximation of an alternative neutrino scattering simulation, NuWro, that shares only a subset of its physics model parameters with GENIE.

Tame-Narvaez, Karla [Fermilab] (ORCID:000000022249↗