Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “randomization”

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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
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 OSTI2020

Multi-fidelity machine-learning with uncertainty quantification and Bayesian optimization for materials design: Application to ternary random alloys

We present a scale-bridging approach based on a multi-fidelity (MF) machine-learning (ML) framework leveraging Gaussian processes (GP) to fuse atomistic computational model predictions across multiple levels of fidelity. Through the posterior variance of the MFGP, our framework naturally enables uncertainty quantification, providing estimates of confidence in the predictions. We used density functional theory as high-fidelity prediction, while a ML interatomic potential is used as low-fidelity prediction. Practical materials’ design efficiency is demonstrated by reproducing the ternary composition dependence of a quantity of interest (bulk modulus) across the full aluminum–niobium–titanium ternary random alloy composition space. The MFGP is then coupled to a Bayesian optimization procedure, and the computational efficiency of this approach is demonstrated by performing an on-the-fly search for the global optimum of bulk modulus in the ternary composition space. The framework presented in this manuscript is the first application of MFGP to atomistic materials simulations fusing predictions between density functional theory and classical interatomic potential calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Demonstration of a 4.32 μ m cutoff InAsSbBi n B n photodetector, a lattice-matched random alloy III–V solution for mid-wave infrared sensing

InAsSbBi nBn photodetectors are demonstrated that are lattice-matched to the underlying GaSb substrate with a 4.32 μm wavelength cutoff at 150 K that is 0.3 μm longer than that of lattice-matched InAsSb at this temperature reflecting a 0.5% Bi mole fraction in the InAsSbBi active region. A low growth temperature was utilized to facilitate the incorporation of Bi, resulting in a minority carrier lifetime on the order of 24 ns in the InAsSbBi active region. Nevertheless, the detectors exhibit a quantum efficiency of 17% at 3.3 μm wavelength with a dark current density of 50 μA/cm2 at 150 K and −0.4 V bias and the strong photoresponse turn-on characteristic of a random alloy at 4.32 μm wavelength and 150 K. A shot noise-equivalent irradiance analysis indicates that this detectors' dark-current-limited noise-equivalent irradiance of 1012 cm−2 s−1 is two orders of magnitude greater than the Rule 07 expectation for this cutoff, and dark-current-limited shot noise-equivalent irradiance performance transitions to photon-limited at 1.7 × 1015 photons/cm2 s.

Physics↗
DOE OSTI2025

The influence of V-defects, leakage, and random alloy fluctuations on the carrier transport in red InGaN MQW LEDs

Red InGaN-based light-emitting diodes (LEDs) exhibit lower internal quantum efficiencies than violet, blue, and green InGaN LEDs due to a reduction in radiative recombination rates relative to non-radiative recombination rates as the Indium composition increases. Additionally, the larger polarization and band offset barriers between high indium content InGaN quantum wells and GaN quantum barriers increase the forward voltage. In blue and green LEDs, random alloy fluctuations and V-defects play a key role in reducing the forward voltage. When V-defects are present, either naturally or intentionally introduced, they create an alternative path for carrier injection into the MQWs through the V-defect sidewalls. This injection mechanism explains the turn-on voltages of green LEDs. However, in InGaN red LEDs, these two phenomena do not reduce the forward voltage as effectively as in blue and green LEDs, and consequently, the computed forward voltage remains significantly higher than the measured one. Furthermore, currents are observed at low voltages before the turn-on voltage (V<ℏω/e=2.0 V) of red LEDs. To address this, we introduce dislocation-induced tail states in the modeling, suggesting that leakage current through these states may play a significant role both below and at turn-on voltages. The simulation also indicates that leakage carriers below turn-on accumulate, partially diffuse in the QWs, screen the polarization-induced barrier in the low injection regime, and further reduce the forward voltage. Despite these beneficial effects, a drawback of dislocation-induced tail states is the enhanced nonradiative recombination in the dislocation line region. This study provides a detailed analysis of device injection physics in InGaN QW red LEDs and outlines potential optimization strategies.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Chaos as the cause of randomness in tearing mode onset times in DIII-D ITER baseline scenario plasmas

The first evidence of widespread chaotic plasma dynamics relevant to the onset of disruptive tearing modes is found in DIII-D tokamak discharges. This evidence is obtained by calculating positive maximal Lyapunov exponents from Mirnov signals using the Rosenstein algorithm, implying the chaotic growth and/or rotation of magnetohydrodynamic modes in DIII-D [Luxon, Nucl. Fusion 42, 614 (2002)] plasmas. Such chaotic dynamics offer an explanation for the random tearing mode onset times previously observed in ITER baseline scenario discharges. Further, the Lyapunov timescales associated with this chaos could inhibit reliable prediction of tearing mode onset beyond the angular momentum confinement timescale; this constraint should inform the design of plasma control systems on ITER.

Chaotic dynamics↗
DOE OSTI2022

Opinion dynamics in financial markets via random networks

We investigate financial market dynamics by introducing a heterogeneous agent-based opinion formation model. In this work, we organize individuals in a financial market according to their trading strategy, namely, whether they are noise traders or fundamentalists. The opinion of a local majority compels the market exchanging behavior of noise traders, whereas the global behavior of the market influences the decisions of fundamentalist agents. We introduce a noise parameter, q , to represent the level of anxiety and perceived uncertainty regarding market behavior, enabling the possibility of adrift financial action. We place individuals as nodes in an Erdös-Rényi random graph, where the links represent their social interactions. At any given time, individuals assume one of two possible opinion states ±1 regarding buying or selling an asset. The model exhibits fundamental qualitative and quantitative real-world market features such as the distribution of logarithmic returns with fat tails, clustered volatility, and the long-term correlation of returns. We use Student’s t distributions to fit the histograms of logarithmic returns, showing a gradual shift from a leptokurtic to a mesokurtic regime depending on the fraction of fundamentalist agents. Furthermore, we compare our results with those concerning the distribution of the logarithmic returns of several real-world financial indices.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A random-sampling method as an efficient alternative to variational Monte Carlo for solving Gutzwiller wavefunctions

Abstract We present a random-sampling (RS) method for evaluating expectation values of physical quantities using the variational approach. We demonstrate that the RS method is computationally more efficient than the variational Monte Carlo method using the Gutzwiller wavefunctions applied on single-band Hubbard models as an example. Non-local constraints can also been easily implemented in the current scheme that capture the essential physics in the limit of strong on-site repulsion. In addition, we extend the RS method to study the antiferromagnetic states with multiple variational parameters for 1D and 2D Hubbard models.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Node-degree aware edge sampling mitigates inflated classification performance in biomedical random walk-based graph representation learning

Motivation: Graph representation learning is a family of related approaches that learn low-dimensional vector representations of nodes and other graph elements called embeddings. Embeddings approximate characteristics of the graph and can be used for a variety of machine-learning tasks such as novel edge prediction. For many biomedical applications, partial knowledge exists about positive edges that represent relationships between pairs of entities, but little to no knowledge is available about negative edges that represent the explicit lack of a relationship between two nodes. For this reason, classification procedures are forced to assume that the vast majority of unlabeled edges are negative. Existing approaches to sampling negative edges for training and evaluating classifiers do so by uniformly sampling pairs of nodes. Results: We show here that this sampling strategy typically leads to sets of positive and negative examples with imbalanced node degree distributions. Using representative heterogeneous biomedical knowledge graph and random walk-based graph machine learning, we show that this strategy substantially impacts classification performance. If users of graph machine-learning models apply the models to prioritize examples that are drawn from approximately the same distribution as the positive examples are, then performance of models as estimated in the validation phase may be artificially inflated. We present a degree-aware node sampling approach that mitigates this effect and is simple to implement. Availability and implementation: Our code and data are publicly available at https://github.com/monarch-initiative/negativeExampleSelection.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Comparison of linear regression, k-nearest neighbour and random forest methods in airborne laser-scanning-based prediction of growing stock

Abstract In this study, for five sites around the world, we look at the effects of different model types and variable selection approaches on forest yield modelling performances in an area-based approach (ABA). We compared ordinary least squares regression (OLS), k-nearest neighbours (kNN) and random forest (RF). Our objective was to test if there are systematic differences in accuracy between OLS, kNN and RF in ABA predictions of growing stock volume. The analyses are based on a 5-fold cross-validation at five study sites: an eucalyptus plantation, a temperate forest and three different boreal forests. Two completely independent validation datasets were also available for two of the boreal sites. For the kNN, we evaluated multiple measures of distance including Euclidean, Mahalanobis, most similar neighbour (MSN) and an RF-based distance metric. The variable selection approaches we examined included a heuristic approach (for OLS, kNN and RF), exhaustive search among all combinations (OLS only) and all variables together (RF only). Performances varied by model type and variable selection approaches among sites. OLS and RF had similar accuracies and were more efficient than any of the kNN variants. Variable selection did not affect RF performance. Heuristic and exhaustive variable selection performed similarly for OLS. kNN fared the poorest amongst model types, and kNN with RF distance was prone to overfitting when compared with a validation dataset. Additional caution is therefore required when building kNN models for volume prediction though ABA, being preferable instead to opt for models based on OLS with some variable selection, or RF with all variables together.

Cosenza, Diogo N.↗
DOE OSTI2021

A machine learning approach to galaxy properties: joint redshift–stellar mass probability distributions with Random Forest

We demonstrate that highly accurate joint redshift–stellar mass probability distribution functions (PDFs) can be obtained using the Random Forest (RF) machine learning (ML) algorithm, even with few photometric bands available. As an example, we use the Dark Energy Survey (DES), combined with the COSMOS2015 catalogue for redshifts and stellar masses. We build two ML models: one containing deep photometry in the griz bands, and the second reflecting the photometric scatter present in the main DES survey, with carefully constructed representative training data in each case. We validate our joint PDFs for 10 699 test galaxies by utilizing the copula probability integral transform and the Kendall distribution function, and their univariate counterparts to validate the marginals. Benchmarked against a basic set-up of the template-fitting code bagpipes, our ML-based method outperforms template fitting on all of our predefined performance metrics. In addition to accuracy, the RF is extremely fast, able to compute joint PDFs for a million galaxies in just under 6 min with consumer computer hardware. Such speed enables PDFs to be derived in real time within analysis codes, solving potential storage issues. As part of this work we have developed galpro 1, a highly intuitive and efficient python package to rapidly generate multivariate PDFs on-the-fly. galpro is documented and available for researchers to use in their cosmology and galaxy evolution studies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Coherent optical imaging of moving objects hidden in a heavily scattering random medium

Experimental results are presented that provide insight into the physics of statistical imaging in heavily scattering random media based on measured speckle correlations as a function of the change in position of a moving object. In this way, definitive interpretation of a rather complex and earlier theory is achieved, making this work an experimental complement to that theory paper []. Motion could be natural, where the set of positions is estimated or separately obtained, or directed, where a mechanical stage can be used to adjust the object's position. In the experiment, a coherent laser illuminates two scattering diffusers, while an object is translated between them in the resulting speckled field and images are collected in a transmission configuration. Results are shown for various objects of differing size and geometry, allowing the theory to be validated and interpreted with new understanding. This work demonstrates imaging opportunities, and applications include material characterization, environmental imaging and sensing, and deep-tissue imaging. Published by the American Physical Society 2025

Hastings, Ryan L. (ORCID:0009000095977807)↗
DOE OSTI2021

Resonant and random excitations on the proton beam in the Large Hadron Collider for active halo control with pulsed hollow electron lenses

We present the results of numerical simulations and experimental studies about the effects of resonant and random excitations on proton losses, emittances, and beam distributions in the Large Hadron Collider (LHC). In addition to shedding light on complex nonlinear effects, these studies are applied to the design of hollow electron lenses (HEL) for active beam halo control. In the High-Luminosity Large Hadron Collider (HL-LHC), a considerable amount of energy will be stored in the beam tails. To control and clean the beam halo, the installation of two hollow electron lenses, one per beam, is being considered. In standard electron-lens operation, a proton bunch sees the same electron current at every revolution. Pulsed electron beam operation (i.e., different currents for different turns) is also considered, because it can widen the range of achievable halo removal rates. For an axially symmetric electron beam, only protons in the halo are excited. If a residual field is present at the location of the beam core, these particles are exposed to time-dependent transverse kicks and to noise. We discuss the numerical simulations and the experiments conducted in 2016 and 2017 at injection energy in the LHC. The excitation patterns were generated by the transverse feedback and damping system, which acted as a flexible source of dipole kicks. Proton beam losses, emittances, and transverse distributions were recorded as a function of excitation patterns and strengths. The resonant excitations induced rich dynamical effects and nontrivial changes of the beam distributions, which, to our knowledge, have not previously been observed and studied in this detail. We conclude with a discussion of the tolerable and achievable residual fields and proposals for further studies.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Metal-insulator transition in a random Hubbard model

In this work, we examine the metal-insulator transition in a half-filled Hubbard model of electrons with random and all-to-all hopping and exchange, and an on-site nonrandom repulsion, the Hubbard $\textit{U}$. We argue that recent numerical results of Cha et al. can be understood in terms of a deconfined critical point between a disordered Fermi liquid and an insulating spin glass. We find a deconfined critical point in a previously proposed large $\textit{M}$ theory, which generalizes the SU(2) spin symmetry to SU($\textit{M}$) and obtains exponents for the electron and spin correlators which agree with those of Cha et al. This critical point is expected to have the maximal quantum Lyapunov exponent. We also present a renormalization group analysis, and argue for the presence of an additional metallic spin glass phase at half-filling and small $\textit{U}$.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗