Search NASA⌕ Search

SEARCH · Search NASA

Results for “randomized sampling”

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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Data-driven probability concentration and sampling on manifold

A new methodology is proposed for generating realizations of a random vector with values in a finite-dimensional Euclidean space that are statistically consistent with a dataset of observations of this vector. The probability distribution of this random vector, while a priori not known, is presumed to be concentrated on an unknown subset of the Euclidean space. A random matrix is introduced whose columns are independent copies of the random vector and for which the number of columns is the number of data points in the dataset. The approach is based on the use of (i) the multidimensional kernel-density estimation method for estimating the probability distribution of the random matrix, (ii) a MCMC method for generating realizations for the random matrix, (iii) the diffusion-maps approach for discovering and characterizing the geometry and the structure of the dataset, and (iv) a reduced-order representation of the random matrix, which is constructed using the diffusion-maps vectors associated with the first eigenvalues of the transition matrix relative to the given dataset. The convergence aspects of the proposed methodology are analyzed and a numerical validation is explored through three applications of increasing complexity. The proposed method is found to be robust to noise levels and data complexity as well as to the intrinsic dimension of data and the size of experimental datasets. Both the methodology and the underlying mathematical framework presented in this paper contribute new capabilities and perspectives at the interface of uncertainty quantification, statistical data analysis, stochastic modeling and associated statistical inverse problems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRSDate not supplied

OceanWATERS Lander Robotic Arm Operation

Ocean Worlds Autonomy Testbed for Exploration Research and Simulation (OceanWATERS) is an open-source simulator for developing onboard autonomy software for robotic exploration of ocean worlds, such as Europa, Enceladus, and Titan, built on the Robot Operating System (ROS) and Gazebo simulation environment. Inevitable ground communication delays increase demand for a high degree of autonomy during excavation, collection and transfer of samples to scientific instruments for in-situ analysis. This paper offers a detailed discussion of the robotic arm design and operation for such autonomous surface exploration, taking as reference the Europa Lander mission. The lander arm, which is designed primarily to acquire icy surface and subsurface samples within the arm’s workspace, is a 6-degree-of-freedom manipulator with two end effectors: a sample excavation tool and a trenching end-effector. The robotic arm’s modes and operations can be summarized as follows: stowed arm, intended as the lander arm default configuration characterized by zero-power consumption; un-stowed arm, target arm configuration after its first deployment; selection and deployment of the end-effector to use next; guarded move, to detect ground level at the desired trenching location; drill ice using the grinder; dig trench at a particular location using the scoop; deliver sample to the sample transfer dock; discard redundant samples. The motion planning tool used for the lander arm is MoveIt, a ROS package. MoveIt uses sampling-based planning and collision checking libraries to determine safe paths. The Rapidly Exploring Random Trees* (RRT*) has been chosen as default planning algorithm as it provides optimal plans with an exponential speed and is guaranteed to find a solution, if feasible solutions exist. Furthermore, this work quantifies and discusses the energy requirements for excavating and collecting samples. In OceanWATERS, force feedback from the terrain, which influences the arm dynamics, is modelled using a discrete element method (DEM) simulation. The DEM and Gazebo software run in parallel and communicate through a co-simulation plugin. This paper presents an analysis and comparison of three DEM open source software (YADE, ESyS-Particle, Project Chrono) for implementation in OceanWATERS and motivates the choice of YADE as most suitable candidate.

Damiana Catanoso↗
DOE OSTI2022

Estimating epistemic uncertainty in soil parameters for nonlinear site response analyses: Introducing the Latin Hypercube Sampling technique

This study quantifies the effects of epistemic uncertainty in soil parameters on nonlinear (NL) site response analysis (SRA) results, validated against the data recorded at a well-instrumented geotechnical downhole array located in Japan. To this end, a one-dimensional soil column model of the Service Hall Array (SHA) near the Kashiwazaki-Kariwa Nuclear Power Plant (KKNPP) is developed using the finite element (FE) program LS-DYNA. The dynamic stress–strain relationship is characterized by a modified two-stage hyperbolic (MTH) NL backbone curve formulation capable of capturing soil behavior at both small- and large-shear strains. The model is then validated against the ground motion recordings to capture the model bias. The uncertainties associated with the shear-wave velocity profile (a small-strain soil property) and soil shear strength (a large-strain soil property) are incorporated in NL SRA to quantify their separate and joint randomization effects on the results. This study proposes using the Latin Hypercube Sampling (LHS) method as an efficient alternative to commonly used methods, such as Standard Monte Carlo (SMC), to account for uncertainty propagation in such reliability analysis. Both low-intensity and design-level records from the recordings at the SHA are applied to study the contribution of the small- and large-strain NL dynamic soil properties. Results from 46,200 NL FE analyses (23,100 per input motion) are presented. Measured and predicted site response, using recorded ground motions at this downhole array, is compared to assess the significance of soil parameter uncertainty on the observed ground motion dispersions. It is demonstrated that increasing the number of soft realizations and implementing higher level earthquake intensity lead to higher ground motion dispersion. Unlike past studies in randomization of Vs profiles with the SMC method, the LHS method is shown to have no significant effect on the predicted median surface response spectra and amplification factors (AFs) for this case study.

Engineering↗
NASA NTRS1982

Linear discriminant analysis with misallocation in training samples

Linear discriminant analysis for a two-class case is studied in the presence of misallocation in training samples. A general appraoch to modeling of mislocation is formulated, and the mean vectors and covariance matrices of the mixture distributions are derived. The asymptotic distribution of the discriminant boundary is obtained and the asymptotic first two moments of the two types of error rate given. Certain numerical results for the error rates are presented by considering the random and two non-random misallocation models. It is shown that when the allocation procedure for training samples is objectively formulated, the effect of misallocation on the error rates of the Bayes linear discriminant rule can almost be eliminated. If, however, this is not possible, the use of Fisher rule may be preferred over the Bayes rule.

Chhikara, R.↗
DOE OSTI2017

CHRR: coordinate hit-and-run with rounding for uniform sampling of constraint-based models

In constraint-based metabolic modelling, physical and biochemical constraints define a polyhedral convex set of feasible flux vectors. Uniform sampling of this set provides an unbiased characterization of the metabolic capabilities of a biochemical network. However, reliable uniform sampling of genome-scale biochemical networks is challenging due to their high dimensionality and inherent anisotropy. Here, we present an implementation of a new sampling algorithm, coordinate hit-and-run with rounding (CHRR). This algorithm is based on the provably efficient hit-and-run random walk and crucially uses a preprocessing step to round the anisotropic flux set. CHRR provably converges to a uniform stationary sampling distribution. We apply it to metabolic networks of increasing dimensionality. We show that it converges several times faster than a popular artificial centering hit-and-run algorithm, enabling reliable and tractable sampling of genome-scale biochemical networks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

SDSS-IV MaNGA: The MaNGA Dwarf Galaxy Sample Presentation

We present the MaNGA Dwarf galaxy (MaNDala) Value Added Catalog (VAC), from the final release of the Sloan Digital Sky Survey-IV program. MaNDala consists of 136 randomly selected bright dwarf galaxies with M * < 10 9.1 M ⊙ and M g > -18.5, making it the largest integral field spectroscopy homogeneous sample of dwarf galaxies. We release a photometric analysis of the g, r, and z broadband imaging based on the DESI Legacy Imaging Surveys, as well as a spectroscopic analysis based on the Pipe3D SDSS-IV VAC. Our release includes the surface brightness (SB), geometric parameters, and color profiles, Sérsic fits as well as stellar population properties (such as stellar ages, metallicities, and star formation histories), and emission lines' fluxes within the FOV and the effective radii of the galaxies. We find that the majority of the MaNDala galaxies are star-forming late-type galaxies with $\langle$n Sersic,r $\rangle$ ~ 1.6 that are centrals (central/satellite dichotomy). MaNDala covers a large range of SB values (we find 11 candidate ultra-diffuse galaxies and three compact ones), filling the gap between classical dwarfs and low-mass galaxies in the Kormendy Diagram and in the size–mass/luminosity relation, which seems to flatten at 10 8 < M * /M ⊙ < 10 9 with $\langle$R e,r $\rangle$ ~ 2.7 kpc. A large fraction of MaNDala galaxies formed from an early low-metallicity burst of SF, but also from late SF events from more metal-enriched gas: half of the MaNDala galaxies assembled 50% of their mass at $\langle$z$\rangle$ > 2, while the last 20% was at $\langle$z$\rangle$ < 0.3. Finally, a bending of the sSFR-M * relation at M * ~ 10 9 M ⊙ for the main-sequence galaxies seems to be supported by MaNDala.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Interpreting High-resolution Spectroscopy of Exoplanets using Cross-correlations and Supervised Machine Learning

We present a new method for performing atmospheric retrieval on ground-based, high-resolution data of exoplanets. Our method combines cross-correlation functions with a random forest, a supervised machine-learning technique, to overcome challenges associated with high-resolution data. A series of cross-correlation functions are concatenated to give a “CCF-sequence” for each model atmosphere, which reduces the dimensionality by a factor of ∼100. The random forest, trained on our grid of ∼65,000 models, provides a likelihood-free method of retrieval. The precomputed grid spans 31 values of both temperature and metallicity, and incorporates a realistic noise model. We apply our method to HARPS-N observations of the ultra-hot Jupiter KELT-9b and obtain a metallicity consistent with solar (logM = − 0.2 ± 0.2). Our retrieved transit chord temperature (T=6000{sub −200}{sup +0}K) is unreliable as strong ion lines lie outside of the extent of the training set, which we interpret as being indicative of missing physics in our atmospheric model. We compare our method to traditional nested sampling, as well as other machine-learning techniques, such as Bayesian neural networks. We demonstrate that the likelihood-free aspect of the random forest makes it more robust than nested sampling to different error distributions, and that the Bayesian neural network we tested is unable to reproduce complex posteriors. We also address the claim in Cobb et al. 2019 that our random forest retrieval technique can be overconfident but incorrect. We show that this is an artifact of the training set, rather than of the machine-learning method, and that the posteriors agree with those obtained using nested sampling.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Improved uncertainty quantification in nondestructive assay for nonproliferation

This paper illustrates methods to improve uncertainty quantification (UQ) for non-destructive assay (NDA) measurements used in nuclear nonproliferation. First, it is shown that current bottom-up UQ applied to calibration data is not always adequate, for three main reasons: (1) Because there are errors in both the predictors and the response, calibration involves a ratio of random quantities, and calibration data sets in NDA usually consist of only a modest number of samples (3–10); therefore, asymptotic approximations involving quantities needed for UQ such as means and variances are often not sufficiently accurate; (2) Common practice overlooks that calibration implies a partitioning of total error into random and systematic error, and (3) In many NDA applications, test items exhibit non-negligible departures in physical properties from calibration items, so model-based adjustments are used, but item-specific bias remains in some data. Therefore, improved bottom-up UQ using calibration data should predict the typical magnitude of item-specific bias, and the suggestion is to do so by including sources of item-specific bias in synthetic calibration data that is generated using a combination of modeling and real calibration data. Second, for measurements of the same nuclear material item by both the facility operator and international inspectors, current empirical (top-down) UQ is described for estimating operator and inspector systematic and random error variance components. A Bayesian alternative is introduced that easily accommodates constraints on variance components, and is more robust than current top-down methods to the underlying measurement error distributions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

High-temperature annealing of macroporous silicon in an inert-gas flow

Interest in the sintering of macroporous silicon is due to the possibility of purposefully modifying its structure. The annealing of macroporous structures in an atmosphere of Ar, instead of H{sub 2}, simplifies the requirements to equipment and safety engineering. The sintering of macroporous silicon as result of annealing at T = 1000–1280°C in a horizontal tube purged with high-purity gases: Ar or Ar + 3%H{sub 2} is examined. Experiments were conducted with layers having deep cylindrical macropores produced by the electrochemical etching of samples with seed pits on their surface (ordered pores) and without seeds (random pores). The morphology of the porous structure and the changes in this structure upon annealing are studied with electron and optical microscopes. It is shown that, depending on the pore diameter and treatment temperature, the following transformation occurs: the pore surface is smoothed, pores are closed and a surface crust is formed, cylindrical pores are spheroidized and decompose into isolated hollow spheres, and a fine structure and faceting are formed. It is shown that the (111) planes have the minimal surface energy. It is found that the annealing of macroporous silicon in an inert gas leads to strong thermal etching, which is manifested in the fact that the porosity increases or even the porous layer at the sample edge fully disappears. Moreover, an oxide layer appears as a film, beads, or long filaments forming a glass wool upon annealing, especially at low temperatures. These features can be attributed to the presence of trace amounts of an oxidizing agent in the inert gas, which causes the formation of highly volatile SiO and products formed in the reaction involving this compound.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Effect of heat treatment on structural and Mössbauer spectroscopic properties of coprecipitated Mn{sub 0.5}Ni{sub 0.5}Fe{sub 2}O{sub 4} ferrite nanoparticles

Results obtained in a systamatic study by X-ray diffraction and Mösssbauer spectroscopy on the structural and magnetic properties on Mn{sub 0.5}Ni{sub 0.5}Fe{sub 2}O{sub 4} ferrite nanoparticles heat treated at 200 °C, 500 °C and 800 °C are reported. Average crystallite sizes are estimated to be in the range (2.6nm – 12.8nm). It is observed that crystallite sizes increase with increase in sintering temperature and random variation in lattice parameter was observed. At relatively low sintering temperatures the samples exhibit superparamagnetism and complete ferrite phase was observed at higher heat treatment.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

MALDI-MS screening of microbial colonies with isomer resolution to select fatty acid desaturase variants

Creating controlled lipid unsaturation locations in oleochemicals can be a key to many bioengineered products. However, evaluating the effects of modifications to the acyl-ACP desaturase on lipid unsaturation is not currently amenable to high-throughput assays, limiting the scale of redesign efforts to <200 variants. Here, we report a rapid MS assay for profiling the positions of double bonds on membrane lipids produced by Escherichia coli colonies after treatment with ozone gas. By MS measurement of the ozonolysis products of Δ6 and Δ8 isomers of membrane lipids from colonies expressing recombinant Thunbergia alata desaturase, we screened a randomly mutagenized library of the desaturase gene at 5 s per sample. Two variants with altered regiospecificity were isolated, indicated by an increase in 16:1 Δ8 proportion. We also demonstrated the ability of these desaturase variants to influence the membrane composition and fatty acid distribution of E. coli strains deficient in the native acyl-ACP desaturase gene, fabA. Lastly, we used the fabA deficient chassis to concomitantly express a non-native acyl- ACP desaturase and a medium-chain thioesterase from Umbellularia californica, demonstrating production of only saturated free fatty acids.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Towards robust surrogate models: Benchmarking machine learning approaches to expediting phase field simulations of brittle fracture

Data-driven approaches have the potential to make modeling complex, nonlinear physical phenomena significantly more computationally tractable. For example, computational modeling of fracture is a core challenge where machine learning techniques have the potential to provide a much needed speedup that would enable progress in areas such as multi-scale modeling and uncertainty quantification. Currently, phase field modeling (PFM) of fracture is one such approach that offers a convenient variational formulation to model crack nucleation, branching and propagation. To date, machine learning techniques have shown promise in approximating PFM simulations. While standard fracture benchmarks represent realistic scenarios frequently observed in practice, they typically do not provide sufficiently challenging tests for data-driven methods. Here, to address this gap, we introduce a challenging dataset based on PFM simulations designed to benchmark and advance ML methods for fracture modeling. This dataset includes three energy decomposition methods, two boundary conditions, and 1000 random initial crack configurations for a total of 6000 simulations. Each sample contains 100 time steps capturing the temporal evolution of the crack field. Alongside this dataset, we also implement and evaluate Physics Informed Neural Networks (PINN), Fourier Neural Operators (FNO), and UNet models as baselines, and explore the impact of ensembling strategies on prediction accuracy. With this combination of our dataset and baseline models drawn from the literature we aim to provide a standardized and challenging benchmark for evaluating machine learning approaches to solid mechanics. Our results highlight both the promise and limitations of popular current models, and demonstrate the utility of this dataset as a testbed for advancing machine learning in fracture mechanics research.

Benchmark dataset↗
DOE OSTI2024

A robust synthetic data generation framework for machine learning in high-resolution transmission electron microscopy (HRTEM)

Machine learning techniques are attractive options for developing highly-accurate analysis tools for nanomaterials characterization, including high-resolution transmission electron microscopy (HRTEM). However, successfully implementing such machine learning tools can be difficult due to the challenges in procuring sufficiently large, high-quality training datasets from experiments. In this work, we introduce Construction Zone, a Python package for rapid generation of complex nanoscale atomic structures which enables fast, systematic sampling of realistic nanomaterial structures and can be used as a random structure generator for large, diverse synthetic datasets. Using Construction Zone, we develop an end-to-end machine learning workflow for training neural network models to analyze experimental atomic resolution HRTEM images on the task of nanoparticle image segmentation purely with simulated databases. Further, we study the data curation process to understand how various aspects of the curated simulated data—including simulation fidelity, the distribution of atomic structures, and the distribution of imaging conditions—affect model performance across three benchmark experimental HRTEM image datasets. Using our workflow, we are able to achieve state-of-the-art segmentation performance on these experimental benchmarks and, further, we discuss robust strategies for consistently achieving high performance with machine learning in experimental settings using purely synthetic data. Construction Zone and its documentation are available at https://github.com/lerandc/construction_zone.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Spin-orbit torque induced magnetization switching in Ta/Co{sub 20}Fe{sub 60}B{sub 20}/MgO structures under small in-plane magnetic fields

Spin-orbit torque (SOT)-induced magnetization switching under small in-plane magnetic fields in as-deposited and annealed Ta/CoFeB/MgO structures is studied. For the as-deposited samples, partial SOT-induced switching behavior is observed under an in-plane field of less than 100 Oe. Conversely, for the annealed samples, an in-plane field of 10 Oe is large enough to achieve full deterministic magnetization switching. The Dzyaloshinskii-Moriya interaction at the Ta/CoFeB interface is believed to be the main reason for the discrepancy of the requisite in-plane magnetic fields for switching in the as-deposited and annealed samples. In addition, asymmetric field dependence behavior of SOT-induced magnetization switching is observed in the annealed samples. Deterministic magnetization switching in the absence of an external magnetic field is obtained in the annealed samples, which is extremely important to develop SOT-based magnetoresistive random access memory.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Reinforcement learning in discrete action space applied to inverse defect design

Abstract Reinforcement learning (RL) algorithms that include Monte Carlo Tree Search (MCTS) have found tremendous success in computer games such as Go, Shiga and Chess. Such learning algorithms have demonstrated super-human capabilities in navigating through an exhaustive discrete action search space. Motivated by their success in computer games, we demonstrate that RL can be applied to inverse materials design problems. We deploy RL for a representative case of the optimal atomic scale inverse design of extended defects via rearrangement of chalcogen (e.g. S) vacancies in 2D transition metal dichalcogenides (e.g. MoS 2 ). These defect rearrangements and their dynamics are important from the perspective of tunable phase transition in 2D materials i.e. 2H (semi-conducting) to 1T (metallic) in MoS 2 . We demonstrate the ability of MCTS interfaced with a reactive molecular dynamics simulator to efficiently sample the defect phase space and perform inverse design—starting from randomly distributed S vacancies, the optimal defect rearrangement of defects corresponds a line defect of S vacancies. We compare MCTS performance with evolutionary optimization i.e. genetic algorithms and show that MCTS converges to a better optimal solution (lower objective) and in fewer evaluations compared to GA. We also comprehensively evaluate and discuss the effect of MCTS hyperparameters on the convergence to solution. Overall, our study demonstrates the effectives of using RL approaches that operate in discrete action space for inverse defect design problems.

42 ENGINEERING↗
DOE OSTI2025

Continuum-electron interferometry for enhancement of photoelectron circular dichroism and measurement of bound, free, and mixed contributions to chiral response

We develop photoelectron interferometry based on laser-assisted extreme ultraviolet ionization for flexible and robust control of photoelectron circular dichroism in randomly oriented chiral molecules. A comb of XUV photons ionizes a sample of chiral molecules in the presence of a time-delayed infrared or visible laser pulse promoting interferences between components of the XUV-ionized photoelectron wave packet. In striking contrast to multicolor phase control schemes relying on pulse shaping techniques, the magnitude of the resulting chiral signal is here controlled by the time delay between the XUV and laser pulses. Furthermore, we show that the relative polarization configurations of the XUV and IR fields allow for disentangling the contributions of bound and continuum states to the chiral response. Our proposal provides a simple, robust, and versatile tool for the control of photoelectron circular dichroism and experimentally feasible protocol for probing the individual contributions of bound and continuum states to the photoelectron circular dichroism in a time-resolved manner.

atomic and molecular processes in external fields↗
DOE OSTI2023

Data for MALDI-MS Screening of Microbial Colonies With Isomer Resolution to Select Fatty Acid Desaturase Variants

Creating controlled lipid unsaturation locations in oleochemicals can be a key to many bioengineered products. However, evaluating the effects of modifications to the acyl-ACP desaturase on lipid unsaturation is not currently amenable to high-throughput assays, limiting the scale of redesign efforts to <200 variants. Here, we report a rapid mass spectrometry (MS) assay for profiling the positions of double bonds on membrane lipids produced by Escherichia coli colonies after treatment with ozone gas. By MS measurement of the ozonolysis products of Δ6 and Δ8 isomers of membrane lipids from colonies expressing recombinant Thunbergia alata desaturase, we screened a randomly mutagenized library of the desaturase gene at 5 s per sample. Two variants with altered regiospecificity were isolated, indicated by an increase in 16:1 Δ8 proportion. We also demonstrated the ability of these desaturase variants to influence the membrane composition and fatty acid distribution of E. coli strains deficient in the native acyl-ACP desaturase gene, fabA. Finally, we used the fabA deficient chassis to concomitantly express a non-native acyl-ACP desaturase and a medium-chain thioesterase from Umbellularia californica, demonstrating production of only saturated free fatty acids.

Conversion↗
NASA NTRS1989

Double galaxy redshifts and the statistics of small numbers

Tifft (1980, 1982) claimed that observations of double galaxies reveal a 72 km/s periodicity. Sharp showed that the 'periodicities' in the observations are completely consistent with the statistics of small numbers. Here it is shown that Tifft's statistical procedure would ascribe a periodicity to small sets of Gaussian random noise. Satisfying the null hypothesis that the observations are not samples drawn from a normal population would require the acquisition of at least an order of magnitude more data.

Newman, William I.↗