Search NASA⌕ Search

SEARCH · Search NASA

Results for “root modeling”

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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Eclipse-Free-Time Assessment Tool for IRIS

IRIS_EFT is a scientific simulation that can be used to perform an Eclipse-Free- Time (EFT) assessment of IRIS (Infrared Imaging Surveyor) mission orbits. EFT is defined to be those time intervals longer than one day during which the IRIS spacecraft is not in the Earth s shadow. Program IRIS_EFT implements a special perturbation of orbital motion to numerically integrate Cowell's form of the system of differential equations. Shadow conditions are predicted by embedding this integrator within Brent s method for finding the root of a nonlinear equation. The IRIS_EFT software models the effects of the following types of orbit perturbations on the long-term evolution and shadow characteristics of IRIS mission orbits. (1) Non-spherical Earth gravity, (2) Atmospheric drag, (3) Point-mass gravity of the Sun, and (4) Point-mass gravity of the Moon. The objective of this effort was to create an in-house computer program that would perform eclipse-free-time analysis. of candidate IRIS spacecraft mission orbits in an accurate and timely fashion. The software is a suite of Fortran subroutines and data files organized as a "computational" engine that is used to accurately predict the long-term orbit evolution of IRIS mission orbits while searching for Earth shadow conditions.

Eagle, David↗
NASA NTRS2017

Full-Coverage High-Resolution Daily PM(sub 2.5) Estimation using MAIAC AOD in the Yangtze River Delta of China

Satellite aerosol optical depth (AOD) has been used to assess population exposure to fine particulate matter (PM (sub 2.5)). The emerging high-resolution satellite aerosol product, Multi-Angle Implementation of Atmospheric Correction(MAIAC), provides a valuable opportunity to characterize local-scale PM(sub 2.5) at 1-km resolution. However, non-random missing AOD due to cloud snow cover or high surface reflectance makes this task challenging. Previous studies filled the data gap by spatially interpolating neighboring PM(sub 2.5) measurements or predictions. This strategy ignored the effect of cloud cover on aerosol loadings and has been shown to exhibit poor performance when monitoring stations are sparse or when there is seasonal large-scale missngness. Using the Yangtze River Delta of China as an example, we present a Multiple Imputation (MI) method that combines the MAIAC high-resolution satellite retrievals with chemical transport model (CTM) simulations to fill missing AOD. A two-stage statistical model driven by gap-filled AOD, meteorology and land use information was then fitted to estimate daily ground PM(sub 2.5) concentrations in 2013 and 2014 at 1 km resolution with complete coverage in space and time. The daily MI models have an average R(exp 2) of 0.77, with an inter-quartile range of 0.71 to 0.82 across days. The overall Ml model 10-fold cross-validation R(exp 2) (root mean square error) were 0.81 (25 gm(exp 3)) and 0.73 (18 gm(exp 3)) for year 2013 and 2014, respectively. Predictions with only observational AOD or only imputed AOD showed similar accuracy.Comparing with previous gap-filling methods, our MI method presented in this study performed bette rwith higher coverage, higher accuracy, and the ability to fill missing PM(sub 2.5) predictions without ground PM(sub 2.5) measurements. This method can provide reliable PM(sub 2.5)predictions with complete coverage that can reduce biasin exposure assessment in air pollution and health studies.

PM2.5↗
NASA NTRS2022

Understanding the Microphysical Control and Spatial-Temporal Variability of Warm Rain Probability Using CloudSat and MODIS Observations

By combining measurements from MODIS and the CloudSat radar, we develop a parameterization scheme to quantify the combined microphysical controls by liquid water path (LWP) and cloud droplet number concentration (CDNC) of the probability of precipitation (PoP) in marine low cloud over tropical oceans. We demonstrate that the spatial-temporal variation of grid-mean in-cloud can be largely explained by the variation of the joint probability density function of LWP and CDNC in the phase space specified by the bivariate PoP (LWP and CDNC) function. Through a series of sensitivity tests guided by this understanding, we find that in the Southeastern Pacific and Atlantic the stratocumulus to cumulus transition of the is mainly due to the variation of CDNC while the annual cycle is mainly due to the variation of LWP. The results of this study provide a viable way to diagnose the root cause of warm rain problems in global climate models.

Zhibo Zhang↗
NASA NTRS2019

Understanding the Impacts of Soil Moisture Initial Conditions on NWP in the Context of Land-Atmosphere Coupling

The role of soil moisture in NWP has gained more attention in recent years, as studies have demonstrated impacts of land surface states on ambient weather from diurnal to seasonal scales. However, soil moisture initialization approaches in coupled models remain quite diverse in terms of their complexity and observational roots, while assessment using bulk forecast statistics can be simplistic and misleading. In this study, a suite of soil moisture initialization approaches is used to generate short-term coupled forecasts over the U.S. Southern Great Plains using NASA’s Land Information System (LIS) and NASA Unified WRF (NU-WRF) modeling systems. This includes a wide range of currently used initialization approaches, including soil moisture derived from ‘off-the-shelf’ products such as atmospheric models and land data assimilation systems, high-resolution land surface model spinups, and satellite-based soil moisture products from SMAP. Results indicate that the spread across initialization approaches can be quite large in terms of soil moisture conditions and spatial resolution, and that SMAP performs well in terms of heterogeneity and temporal dynamics when compared against high resolution land surface model and in-situ soil moisture estimates. Case studies are analyzed using the local land-atmosphere coupling (LoCo) framework that relies on integrated assessment of soil moisture, surface flux, boundary layer, and ambient weather, with results highlighting the critical role of inherent model background biases. In addition, simultaneous assessment of land vs. atmospheric initial conditions in an integrated, process-level fashion can help address the question of whether improvements in traditional NWP verification statistics are achieved for the right reasons.

Southern Great Plains↗
DOE OSTI2024

Refining water and carbon fluxes modeling in terrestrial ecosystems via plant hydraulics integration

Plant hydraulics substantially affects terrestrial water and carbon cycles by modulating water transport and carbon assimilation. Despite improved drought simulations in certain ecosystems through their integration into land surface models (LSMs), the broader application of plant hydraulics in diverse ecosystems and hydroclimates is still underexplored. Here, in this study, we implemented the recently developed Noah-Multiparameterization Land Surface Model (Noah-MP LSM) equipped with a plant hydraulics scheme (Noah-MP-PHS) across 40 FLUXNET sites globally. Employing the Shuffled Complex Evolution-University of Arizona (SCE-UA) auto-calibration algorithm, we optimized key plant hydraulics parameters for these sites spanning eight vegetation types in both arid and humid climates. Noah-MP-PHS significantly improves the simulation of evapotranspiration (ET) and gross primary production (GPP) by better representing atmospheric and soil water stress compared to traditional soil hydraulic schemes (SHSs, such as Noah and CLM). The augmented Noah-MP-PHS models reduce surface flux overestimation and underestimation, exhibiting an average increase of 0.14 and 0.15 in Kling-Gupta Efficiency (KGE) compared to Noah and CLM, respectively. The explicit consideration of plant capacitance in PHS reveals substantial deep-layer and nocturnal root water uptake especially under dry conditions. We employed eXplainable Machine learning (XML) to quantify the model’s relative sensitivity to newly introduced leaf-, stem and root-related parameters in PHS. The sensitivity analysis reveals a rise in root parameter importance and a decline in leaf and stem parameters as conditions shift from humid to arid. These findings indicate that as aridity states vary, the most influential parameters affecting surface fluxes variation may change in parameter calibration for PHS applications. Our findings underscore the importance of incorporating plant hydraulics into LSMs to enhance simulations of terrestrial water and carbon dynamics. These findings are crucial for understanding ecosystem responses to global climate changes and guide the broader application of PHS at larger scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Non-Invasive Electrode Impedance Estimation for Optimized Charge Profile Parameterization of Lithium-Ion Batteries

This work presents a non-invasive method for parameterizing a physically motivated equivalent circuit model of lithium-ion batteries using operando electrochemical impedance spectroscopy and time-domain data. The proposed model consists exclusively of linear circuit elements, enabling computationally efficient simulation and real-time implementation on battery management system chips. By integrating frequency- and time-domain measurements, the model accurately estimates internal states such as the negative electrode potential, achieving a root mean square error of 12.3 mV during fast charging validation. Parameterization requires only rate tests with sinusoidal perturbations at three different ambient temperatures, making the approach experimentally accessible. The model reveals key insights into battery behavior, including rate-dependent overpotentials primarily governed by charge transfer kinetics at the positive electrode, and temperature-dependent impedance contributions from both charge transfer and solid-state diffusion processes. Validation using reference electrodes confirms the model’s ability to detect lithium plating onset and reproduce impedance behavior across a wide range of operating conditions. The approach enables in situ optimization of fast charging profiles and lays the foundation for future extensions incorporating aging effects and plating dynamics.

25 ENERGY STORAGE↗
NASA NTRS1990

Measured aerodynamic performance of a swept wing with a simulated ice accretion

The effect of a simulated glaze ice accretion on the aerodynamic performance of a three-dimensional swept wing is studied experimentally. A semispan wing of effective aspect ratio four was mounted from a splitter plate in the OSU subsonic wind tunnel. The model uses a NACA 0012 airfoil section on a rectangular planform with interchangeable tip and root pieces to allow for 0- and 30-degree sweep. Surface pressures were measured at 5 semispan locations on the swept-wing model. The section lift data for the 5 semispan locations are presented and compared with theory and zero-sweep model results. In addition, previously unpublished roughness data from the zero-sweep model is presented here. These data show a large drag and maximum lift penalty due to rough simulated glaze ice. The effect of wing-sweep is seen through a decrease in wing lift performance for both the clean and iced wing.

Khodadoust, A.↗
DOE OSTI2023

Evolving radon diffusion through earthen barriers at uranium waste disposal sites

Field measurements of Rn-222 fluxes from the tops and bottoms of compacted clay radon barriers were used to calculate effective Rn diffusion coefficients (D Rn ) at four uranium waste disposal sites in the western United States to assess cover performance after more than 20 years of service. Values of D Rn ranged from 7.4 × 10 -7 to 6.0 × 10 -9 m 2 /s, averaging 1.42 × 10 -7 . Water saturation (S W ) from soil cores indicated that there was relatively little control of D Rn by S W , especially at higher moisture levels, in contrast to estimates from most steady-state diffusion models. Further, this is attributed to preferential pathways intrinsic to construction of the barriers or to natural process that have developed over time including desiccation cracks, root channels, and insect burrows in the engineered earthen barriers. A modification to some models in which fast and slow pathway D Rn values are partitioned appears to give a good representation of the data; 4% of the fast pathway was needed to fit the data regression. For locations with high S w and highest D Rn (and fluxes) at each site, the proportion of fast pathway ranged from 1.7% to 34%, but for many locations with lower fluxes, little if any fast pathway was needed.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2016

Snow Process Estimation Over the Extratropical Andes Using a Data Assimilation Framework Integrating MERRA Data and Landsat Imagery

A data assimilation framework was implemented with the objective of obtaining high resolution retrospective snow water equivalent (SWE) estimates over several Andean study basins. The framework integrates Landsat fractional snow covered area (fSCA) images, a land surface and snow depletion model, and the Modern Era Retrospective Analysis for Research and Applications (MERRA) reanalysis as a forcing data set. The outputs are SWE and fSCA fields (1985-2015) at a resolution of 90 m that are consistent with the observed depletion record. Verification using in-situ snow surveys showed significant improvements in the accuracy of the SWE estimates relative to forward model estimates, with increases in correlation (0.49-0.87) and reductions in root mean square error (0.316 m to 0.129 m) and mean error (-0.221 m to 0.009 m). A sensitivity analysis showed that the framework is robust to variations in physiography, fSCA data availability and a priori precipitation biases. Results from the application to the headwater basin of the Aconcagua River showed how the forward model versus the fSCA-conditioned estimate resulted in different quantifications of the relationship between runoff and SWE, and different correlation patterns between pixel-wise SWE and ENSO. The illustrative results confirm the influence that ENSO has on snow accumulation for Andean basins draining into the Pacific, with ENSO explaining approximately 25% of the variability in near-peak (1 September) SWE values. Our results show how the assimilation of fSCA data results in a significant improvement upon MERRA-forced modeled SWE estimates, further increasing the utility of the MERRA data for high-resolution snow modeling applications.

SWE↗
DOE OSTI2025

Sequential Precipitation Input Tagging (SPIT) to Estimate Water Transit Times and Hydrologic Tracer Dynamics Within Water‐Tagging Enabled Hydrologic Models

Determining the age distribution of water exiting a catchment is important for understanding groundwater storage and mixing. New water-tagging capabilities within models track precipitation events as they move through simulated storages, yet forward modeling of individual events may not systematically capture the full transit time distribution (TTD). Here, we present a “sequential precipitation input tagging” (SPIT) framework to tag all input precipitation at regular intervals during extended model simulations. Monthly tags over 7 years were applied at six National Ecological Observatory Network sites to calculate TTDs and derive mean virtual tracer age, $\overline{T_{V}}$, fractions of young water, F yw , and hydrologic tracer concentrations (water isotopes δ 18 O and δ 2 H) within a tagging enabled version of the Weather Research and Forecast hydrologic model (WRF-Hydro). Throughout seven simulation years, the fraction of simulated discharge derived from tagged events, F tag , increased each year, with the final year's F tag ranging from 66% to 100% and highlights the need to apply SPIT over many years to understand TTDs. When the F tag was >75%, simulated $\overline{T_{V}}$ ranged 179–923 days and F yw 0.6%–23.9%, with daily values exhibiting a power-law relationship with precipitation, discharge, and groundwater. Through implementation of SPIT, we find this hydrologic model configuration performs poorly in estimation of $\overline{T_{V}}$ and F yw (root mean squared error of 469 days and 14.4% respectively), suggesting it misrepresents subsurface mixing. Thus, the SPIT framework provides a reproducible approach to calculate watershed transit times within tagging enabled models and thereby assess and improve representation of hydrologic processes.

fraction of young water↗
DOE OSTI2023

Belowground Biomass and Root:Shoot Ratios of Three Willow Cultivars at Two Sites

Belowground biomass is an important but less studied component of energy crop systems that is essential in understanding the greenhouse gas benefits of these systems. In this study, a complete above- and belowground biomass inventory (foliage, stems, stools, coarse and fine roots) was performed on three cultivars of short-rotation willow biomass crops at two sites. Mixed models were used to analyze the proportion of biomass allocated to each component and the ratios between different components. The root:shoot (R:S) ratio, defined here as the stable unharvested biomass (stool and coarse roots) divided by the shoot biomass, averaged 0.63 (SE: +0.04). Though the portion of the plant where the willows distributed their belowground biomass varied, the R:S ratio was not significantly different across sites (p = 0.8970), cultivars (p = 0.2834), nor in the site × cultivar interaction (p = 0.8481). These results may be associated with the consistently good growth across sites and limited differences in site conditions. However, the R:S ratios were affected by the overall productivity of the stand (p = 0.0978), with higher producing stands having moderately lower ratios. This information on biomass allocation between components is essential for understanding and estimating the carbon balance of these systems and breeding and selection programs.

09 BIOMASS FUELS↗
DOE OSTI2021

Constraints on Λ CDM extensions from the SPT-3G 2018 $EE$ and $TE$ power spectra

Here, we present constraints on extensions to the Λ CDM cosmological model from measurements of the E-mode polarization autopower spectrum and the temperature-E-mode cross-power spectrum of the cosmic microwave background (CMB) made using 2018 SPT-3G data. The extensions considered vary the primordial helium abundance, the effective number of relativistic degrees of freedom, the sum of neutrino masses, the relativistic energy density and mass of a sterile neutrino, and the mean spatial curvature. We do not find clear evidence for any of these extensions, from either the SPT-3G 2018 dataset alone or in combination with baryon acoustic oscillation and Planck data. None of these model extensions significantly relax the tension between Hubble-constant, H 0 , constraints from the CMB and from distance-ladder measurements using Cepheids and supernovae. The addition of the SPT-3G 2018 data to Planck reduces the square-root of the determinants of the parameter covariance matrices by factors of 1.3–2.0 across these models, signaling a substantial reduction in the allowed parameter volume. We also explore CMB-based constraints on H 0 from combined SPT, Planck, and ACT DR4 datasets. While individual experiments see some indications of different H 0 values between the TT, TE, and EE spectra, the combined H 0 constraints are consistent between the three spectra. For the full combined datasets, we report H 0 = 67.49 ± 0.53 km s -1 Mpc -1 , which is the tightest constraint on H0 from CMB power spectra to date and in 4.1σ tension with the most precise distance-ladder-based measurement of H 0 . The SPT-3G survey is planned to continue through at least 2023, with existing maps of combined 2019 and 2020 data already having ~ 3.5 x lower noise than the maps used in this analysis.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1994

The relationship between the microwave radar cross section and both wind speed and stress: Model function studies using Frontal Air-Sea Interaction Experiment data

The Frontal Air-Sea Interaction Experiment (FASINEX) provided a unique data set with coincident airborne scatterometer measurements of the ocean surface radar cross section (RCS)(at Ku band) and near-surface wind and wind stress. These data have been analyzed to study new model functions which relate wind speed and surface friction velocity (square root of the kinematic wind stress) to the radar cross section and to better understand the processes in the boundary layer that have a strong influence on the radar backscatter. Studies of data from FASINEX indicate that the RCS has a different relation to the friction velocity than to the wind speed. The difference between the RCS models using these two variables depends on the polarization and the incidence angle. The radar data have been acquired from the Jet Propulsion Laboratory airborne scatterometer. These data span 10 different flight days. Stress measurements were inferred from shipboard instruments and from aircraft flying at low altitudes, closely following the scatterometer. Wide ranges of radar incidence angles and environmental conditions needed to fully develop algorithms are available from this experiment.

Weissman, David E.↗
DOE OSTI2019

Hierarchical Multi-scale Modeling of Laser Weld Failure: An Initial Investigation

The failure of 304L laser welds is of interest to system and component designers due to nuclear safety requirements for abnormal environments. Accurately modeling laser weld behavior in full system and component models has proven especially challenging due to three factors: the large variability observed in laser weld characterization tests; the difficulty in isolating the weld material for material characterization and modeling the weld material behavior; and the disparate scales associated with modeling laser welds in large systems. Recent work has shown that meso-scale geometric features of laser welds such as pores and weld root tortuosity are critical to accurately predicting the structural performance of welds. The challenge with modeling these welds is that the geometric features driving their structural performance are generally on the order of ten to hundreds of microns, but can affect the responses of interest in systems and components on the order of centimeters to meters.

36 MATERIALS SCIENCE↗
NASA NTRS2015

Light-Curve Modelling Constraints on the Obliquities and Aspect Angles of the Young Fermi Pulsars

In more than four years of observation the Large Area Telescope on board the Fermi satellite has identified pulsed gamma-ray emission from more than 80 young or middle-aged pulsars, in most cases providing light curves with high statistics. Fitting the observed profiles with geometrical models can provide estimates of the magnetic obliquity alpha and of the line of sight angle zeta, yielding estimates of the radiation beaming factor and radiated luminosity. Using different gamma-ray emission geometries (Polar Cap, Slot Gap, Outer Gap, One Pole Caustic) and core plus cone geometries for the radio emission, we fit gamma-ray light curves for 76 young or middle-aged pulsars and we jointly fit their gamma-ray plus radio light curves when possible. We find that a joint radio plus gamma-ray fit strategy is important to obtain (alpha, zeta) estimates that can explain simultaneously detectable radio and gamma-ray emission: when the radio emission is available, the inclusion of the radio light curve in the fit leads to important changes in the (alpha, gamma) solutions. The most pronounced changes are observed for Outer Gap and One Pole Caustic models for which the gamma-ray only fit leads to underestimated alpha or zeta when the solution is found to the left or to the right of the main alpha-zeta plane diagonal respectively. The intermediate-to-high altitude magnetosphere models, Slot Gap, Outer Gap, and One pole Caustic, are favored in explaining the observations. We find no apparent evolution of α on a time scale of 106 years. For all emission geometries our derived gamma-ray beaming factors are generally less than one and do not significantly evolve with the spin-down power. A more pronounced beaming factor vs. spin-down power correlation is observed for Slot Gap model and radio-quiet pulsars and for the Outer Gap model and radio-loud pulsars. The beaming factor distributions exhibit a large dispersion that is less pronounced for the Slot Gap case and that decreases from radio-quiet to radio-loud solutions. For all models, the correlation between gamma-ray luminosity and spin-down power is consistent with a square root dependence. The gamma-ray luminosities obtained by using the beaming factors estimated in the framework of each model do not exceed the spin-down power. This suggests that assuming a beaming factor of one for all objects, as done in other studies, likely overestimates the real values. The data show a relation between the pulsar spectral characteristics and the width of the accelerator gap. The relation obtained in the case of the Slot Gap model is consistent with the theoretical prediction.

neutron aEuro" pulsars:↗
DOE OSTI2020

Carbon flow through energycane agroecosystems established post-intensive agriculture

As part of an integrated energy and climate system, biomass production for bioenergy based on the tropical perennial C4 grass energycane can both offset fossil fuels and store soil carbon (C). We measured energycane yields, root biomass, soil C pools, and soil C stocks in a 4 year field trial and modeled C flow from plants to soils in the surface layer of no-till energycane planted after more than a century of intensive sugarcane agriculture. Aboveground yields ranged from 16.7 to 19.0 Mg C/ha over the 4 year trial. Although total C stocks did not significantly differ in the surface layer (approx. 0–20 cm) during the study, C in free and occluded light fractions decreased, whereas C in the mineral-rich dense fraction increased over 4 years. Belowground system inputs, estimated from measurements and informed by convergence in the final soil fraction model, were set to 2.5 Mg C ha -1 year -1 . With this input value, we estimated that surface soils retained photosynthetically fixed C predominantly within the mineral-associated organic matter pool for a mean and median transit time of 177 and 110 years, respectively. Although we did not model C flow to deep soil layers (approx. 0–100 cm), observed C accumulation (11.4 Mg C ha -1 year -1 ) and root growth down to 120 cm suggest that soil processes and resulting C sequestration at the surface are likely to persist deeper into the soil profile. Energycane, as a strong candidate for climate change mitigation and land degradation remediation, showed high biomass yields and allocation of resources to roots, with sequestered soil C expected to persist for over a century.

09 BIOMASS FUELS↗
NASA NTRS1995

Neurodynamic oscillators

Oscillation of electrical activity has been found in many nervous systems, from invertebrates to vertebrates including man. There exists experimental evidence of very simple circuits with the capability of oscillation. Neurons with intrinsic oscillation have been found and also neural circuits where oscillation is a property of the network. These two types of oscillations coexist in many instances. It is nowadays hypothesized that behind synchronization and oscillation there is a system of coupled oscillators responsible for activities that range from locomotion and feature binding in vision to control of sleep and circadian rhythms. The huge knowledge that has been acquired on oscillators from the times of Lord Rayleigh has made the simulation of neural oscillators a very active endeavor. This has been enhanced with more recent physiological findings about small neural circuits by means of intracellular and extracellular recordings as well as imaging methods. The future of this interdisciplinary field looks very promising; some researchers are going into quantum mechanics with the idea of trying to provide a quantum description of the brain. In this work we describe some simulations using neuron models by means of which we form simple neural networks that have the capability of oscillation. We analyze the oscillatory activity with root locus method, cross-correlation histograms, and phase planes. In the more complicated neural network models there is the possibility of chaotic oscillatory activity and we study that by means of Lyapunov exponents. The companion paper shows an example of that kind.

Espinosa, Ismael↗
DOE OSTI2020

Fast and stable deep-learning predictions of material properties for solid solution alloys

We present a novel deep learning (DL) approach to produce highly accurate predictions of macroscopic physical properties of solid solution binary alloys and magnetic systems. The major idea is to make use of the correlations between different physical properties in alloy systems to improve the prediction accuracy of neural network (NN) models. We use multitasking NN models to simultaneously predict the total energy, charge density and magnetic moment. These physical properties mutually serve as constraints during the training of the multitasking NN, resulting in more reliable DL models because multiple physics properties are correctly learned by a single model. Two binary alloys, copper–gold (CuAu) and iron–platinum (FePt), were studied. Our results show that once the multitasking NN's are trained, they can estimate the material properties for a specific configuration hundreds of times faster than first-principles density functional theory calculations while retaining comparable accuracy. We used a simple measure based on the root-mean-squared errors to quantify the quality of the NN models, and found that the inclusion of charge density and magnetic moment as physical constraints leads to more stable models that exhibit improved accuracy and reduced uncertainty for the energy predictions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗