Results for “random sampling” — Page 33

DOE OSTI2025

Quantum algorithm to simulate Lindblad master equations

We present a quantum algorithm for simulating a family of Markovian master equations that can be realized through a probabilistic application of unitary channels and state preparation. Our approach employs a second-order product formula for the Lindblad master equation, achieved by decomposing the dynamics into dissipative and Hamiltonian components and replacing the dissipative segments with randomly compiled, easily implementable elements. The sampling approach eliminates the need for ancillary qubits to simulate the dissipation process and reduces the gate complexity in terms of the number of jump operators. We provide a rigorous performance analysis of the algorithm. We also extend the algorithm to time-dependent Lindblad equations, generalize the family of Markovian master equations it can be applied to, and explore applications beyond the Markovian noise model. A new error bound, in terms of the diamond norm, for second-order product formulas for time-dependent Liouvillians is provided that might be of independent interest. Published by the American Physical Society 2025

Borras, Evan (ORCID:000900017709037X)↗

DOE OSTI2020

Uncertainty Quantification in Scientific ML

The intricate interactions between data sampling, model selection and the inherent randomness in complex systems strongly emphasize the need for a rigorous characterization of ML algorithms. In conventional statistics, uncertainty quantification (UQ) provides this characterization by measuring how accurately a model reflects the physical reality and by studying the impact of different error sources on the prediction.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

(U) Correlated Sampling Using Batch Statistics to Reduce the Uncertainty of Combinations of KSEN Outputs with MCNP6

The relative sensitivity of k eff to the densities of nuclides in a material are combined to compute relative sensitivities to other inputs. When computed in a single Monte Carlo run, the nuclide density sensitivities are correlated, and the statistical uncertainties propagated to other inputs will be incorrect unless those correlations are accounted for. Equations are presented to apply correlated sampling using batch statistics for the sum of an arbitrary number of random tallies and the difference of two random tallies when each is multiplied by a different constant. When correlated sampling is used on a recent benchmark evaluation, the correct statistical uncertainties for certain combinations of sensitivities are dramatically smaller than the incorrect uncertainties. A user-controlled, regular output of MCNP6’s KSEN sensitivities that allows batch statistics to be applied to combinations would be extremely valuable.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS1970

A general approach for generating natural random variables

Random numbers generation from natural random phenomena, discussing advantages, circuit and sampling rate

Murry, H. F.↗

NASA NTRS1973

Preliminary stratigraphy of the Apollo 15 drill core

The crew of Apollo 15 collected a 242-cm-long core of the regolith developed on the surface of Palus Putredinus (3 deg 39 min 20 sec E, 26 deg 26 min 00 sec N). This core consists of 42 major textural units, which range from a few millimeters to 13 cm thick. The regolith at this site was deposited layer by layer, each layer having been reworked, to some degree, by micrometeorite bombardment. The particle composition of six layers for which samples were obtained varies in a random fashion, thus supporting the heterogeneous, layered model of the regolith. The ratio of lithic fragments derived from the Apennine Front to that derived from the mare surface is about 1:1. This lithic ratio, in a regolith section 3.75 km from the base of the front, is an indication of the efficiency of lateral transport of soil by impact processes and mass wasting.

Heiken, G.↗

NASA NTRS1984

The bulk-Moon MgO/FeO ratio: A highlands perspective

Compositional data for nonmare (highlands) samples suggest that the Moon's mg ratio (MgO/FeO) is higher than general estimates. Geochemically representative highlands soils have mg ratios of 0.66 (Apollo 16), 0.69 (Luna 20) and 0.73 (ALHA81005). These soils are mixtures of unrelated pristine nonmare rocks, of which there are at least three groups: Mg-rich rocks, ferroan anorthosites, and KREEP. Other than Mg-rich rocks, virtually all pristine rocks have mg 0.65. Thus, assuming the mixing process that sampled Mg-rich materials was random, the average mg of Mg-rich parent magmas was probably at least 0.70. More direct evidence can be derived from the Mg-rich rocks themselves. Nine of them have bulk-rock mg 0.87. Two (15445 A and 67435 PST) contain Fo(92) olivine. Production of melts that crystallized Fo(92) olivine implies that the mg ratios of source regions in lunar mantle were commensurably high. A quantification of this constraint is developed assuming that the parent melts formed by equilibrium (batch) partial melting. Implications of the model are discussed.

Warren, P. H.↗

NASA NTRS1989

Evaluation of a procedure for the analysis of nonstationary vibroacoustic data

Numerical techniques for the spectral analysis of vibration data from space-vehicle launches are described and demonstrated. A nonstationary product model described by Bendat and Piersol (1986) and its locally stationary version (Silverman, 1957) are applied to Space Shuttle flight data, and the results are presented in extensive graphs. It is shown that the nonstationary model can analyze data from longer sampling periods and thus significantly reduce random error; this in turn leads to vibration spectra lower than those obtained with short-duration models.

Himelblau, Harry↗

NASA NTRS1993

Double troughs in broad absorption line quasars and Ly-alpha-N V line-locking

It was investigated whether the double trough (DT) structure in the mean C IV BAL trough reported by Weymann et al. (1991) is real or due to statistical fluctuations of BAL troughs over random outflow velocities in a limited sample. A sample of 72 BAL QSOs with C IV BAL troughs was analyzed. It is found that only 22 percent of the sample explicitly exhibits the DT feature; when present the DTs are deep. A Monte Carlo simulation of the mean C IV BAL suggests that the DT feature is real at only the 95-98 percent level.

Korista, Kirk T.↗

NASA NTRS2005

Pharmacodynamics of Promethazine in Human Subjects

Promethazine (PMZ) is the drug of choice for the treatment of symptoms associated with space motion sickness in astronauts. Side effects of PMZ include sedation, dizziness and cognitive performance impairment. In this study, we examined pharmacodynamics (PD) in human subjects and validated methods for evaluating cognitive performance effects of medications in space. METHODS: PMZ (12.5,25, and 50 mg) or placebo was administered by IM injection to human subjects in a randomized double-blind treatment design. Samples and data were collected for 72 h post dose. PD evaluation was performed using a battery of performance tests administered using WinSCAT (Windows based Space Cognitive Assessment Test) on a laptop computer, and ARES (ANAM Readiness Evaluation System) on a PDA, plasma concentrations of PMZ were measured using a LC-MS method. RESULTS: Results indicate a linear correlation between PMZ concentration and cognitive performance parameters (p<0.01). Test accuracy decreased and test completion time and response time increased significantly with increasing plasma PMZ concentration. CONCLUSIONS: These results suggest a concentration dependent decrement in cognitive performance associated with PMZ. WinSCAT and ARES are sensitive tools for the assessment PMZ PD and may be applicable for such evaluations with other neurocognitive drugs.

Gatlin, K. T.↗

NASA NTRSDate not supplied

Artificial Neural Networks to Predict Cognitive Impairment of Rodents Subjected to Space Radiation

INTRODUCTION We use artificial neural networks (ANNs) as an example machine learning (ML) tool to predict the cognitive performance impairment of rats induced by irradiation. The experimental data in the analyses is attentional set-shifting (ATSET) test scores from a rodent model exposed to ≤15 cGy of individual galactic cosmic radiation (GCR) ions: 4He, 28Si, or 56Fe, expected for a Lunar or Mars mission [1]. This work investigates rats at a subject-based level and uses applied dose and performance scores taken before irradiation to predict whether a rat will be impaired when irradiated. The results of this study are significant to crewed space missions as they support the potential of predicting an astronaut’s impairment in a specific task before spaceflight through the implementation of appropriately trained ML tools. METHODS Data used in this work are scores from the ATSET, a multi-stage constrained cognitive flexibility test [2]. Our computational model utilizes the number of attempts to reach the criterion to pass a stage as a behavioral performance measure for rats. We use the post-irradiation scores, generate thresholds from cumulative distribution plots of non-irradiated rats, and calculate the percent of irradiated rats whose scores fall below the threshold to infer how each radiation type/dose affects a population. Rats scoring above the threshold are labeled impaired while the others are non-impaired. We then employ ANNs as a typical ML technique, and use each subject’s individual scores taken before radiation along with the applied dose, to predict their personal susceptibility to cognitive impairment due to space radiation exposure. RESULTS AND CONCLUSION A significant finding is the exhibition of a dose-dependent increasing probability of impairment for 1 to 10 cGy of 28Si or 56Fe in the simple discrimination (SD) stage of the ATSET, and for 1 to 10 cGy of 56Fe in the compound discrimination (CD) stage. On a subject-based level, implementing ML classifiers such as ANNs identifies rats that have a higher tendency for impairment after GCR exposure [1]. The receiver operating characteristic (ROC) and the precision-recall (PR) curves of the ML models show a better prediction of impairment when 56Fe is the ion in question in both SD (Figure 1) and CD stages. They, however, do not depict impairment due to 4He in SD (Figure 1) and 28Si in CD, suggesting no dose-dependent impairment response in these cases. In this work, “good” prediction pertains to “better-than-random-chance”, due to the limited sample size and the high inter- and intra-individual variabilities in response to brain stimulation paradigms, as applicable to both animals and humans. More behavioral tests and biomarkers should be investigated on the same subjects, to be fed to the ML models to capture the agents responsible for performance alterations of some individuals versus others.

machine learning↗

NASA NTRSDate not supplied

Machine Learning Models to Predict Cognitive Impairment of Rodents Subjected to Space Radiation

INTRODUCTION We use artificial neural networks (ANNs) as an example machine learning (ML) tool to predict the cognitive performance impairment of rats induced by irradiation. The experimental data in the analyses is attentional set-shifting (ATSET) test scores from a rodent model exposed to ≤15 cGy of individual galactic cosmic radiation (GCR) ions: 4He, 28Si, or 56Fe, expected for a Lunar or Mars mission [1]. This work investigates rats at a subject-based level and uses applied dose and performance scores taken before irradiation to predict whether a rat will be impaired when irradiated. The results of this study are significant to crewed space missions as they support the potential of predicting an astronaut’s impairment in a specific task before spaceflight through the implementation of appropriately trained ML tools. METHODS Data used in this work are scores from the ATSET, a multi-stage constrained cognitive flexibility test [2]. Our computational model utilizes the number of attempts to reach the criterion to pass a stage as a behavioral performance measure for rats. We use the post-irradiation scores, generate thresholds from cumulative distribution plots of non-irradiated rats, and calculate the percent of irradiated rats whose scores fall below the threshold to infer how each radiation type/dose affects a population. Rats scoring above the threshold are labeled impaired while the others are non-impaired. We then employ ANNs as a typical ML technique, and use each subject’s individual scores taken before radiation along with the applied dose, to predict their personal susceptibility to cognitive impairment due to space radiation exposure. RESULTS AND CONCLUSION A significant finding is the exhibition of a dose-dependent increasing probability of impairment for 1 to 10 cGy of 28Si or 56Fe in the simple discrimination (SD) stage of the ATSET, and for 1 to 10 cGy of 56Fe in the compound discrimination (CD) stage. On a subject-based level, implementing ML classifiers such as ANNs identifies rats that have a higher tendency for impairment after GCR exposure [1]. The receiver operating characteristic (ROC) and the precision-recall (PR) curves of the ML models show a better prediction of impairment when 56Fe is the ion in question in both SD (Figure 1) and CD stages. They, however, do not depict impairment due to 4He in SD (Figure 1) and 28Si in CD, suggesting no dose-dependent impairment response in these cases. In this work, “good” prediction pertains to “better-than-random-chance”, due to the limited sample size and the high inter- and intra-individual variabilities in response to brain stimulation paradigms, as applicable to both animals and humans. More behavioral tests and biomarkers should be investigated on the same subjects, to be fed to the ML models to capture the agents responsible for performance alterations of some individuals versus others.

machine learning↗

NASA NTRS2023

Front Range Wildland Fires: Evaluating the Efficacy of Remote Sensing Imagery in Monitoring Forest Fuels Treatment Methods

Over the last several decades, wildfire frequency and severity in forested areas along Colorado’s Front Range have increased due to a buildup of fuels. This has led to an increase in forest treatments, as well as an increased need to evaluate the success of these treatments. Remote sensing products offer an efficient and cost-effective way to monitor forest treatments; however, not all remote sensing products and analysis techniques have been explored by Coloradan land managers. Specifically, project partners at the Colorado State Forest Service (CSFS) and the Colorado Forest Restoration Institute (CFRI) were interested in using an effective and streamlined method of mapping canopy cover to better monitor forest treatment success. To support their needs, the NASA DEVELOP Front Range Wildland Fires team explored National Agricultural Imagery Program (NAIP) imagery at different spatial resolutions and numbers of training points with NASA’s Shuttle Radar Topography Mission (SRTM) Data Elevation Model (DEM) as a predictor in addition to NAIP imagery spectral predictors. From this analysis, we created classified canopy cover rasters, and compared accuracy metrics across model iterations. We also determined that the best performing model, with an overall accuracy of 0.900 uses 2021 NAIP imagery at 2-meter resolution, 800 training points, 200 testing points, does not use topographic predictors, and reclassifies shadow pixels via a pre-selected NDVI threshold.

Remote Sensing↗

DOE OSTI2021

Mapping Stochastic Devices to Probabilistic Algorithms

Probabilistic and Bayesian neural networks have long been proposed as a method to incorporate uncertainty about the world (both in training data and operation) into artificial intelligence applications. One approach to making a neural network probabilistic is to leverage a Monte Carlo sampling approach that samples a trained network while incorporating noise. Such sampling approaches for neural networks have not been extensively studied due to the prohibitive requirement of many computationally expensive samples. While the development of future microelectronics platforms that make this sampling more efficient is an attractive option, it has not been immediately clear how to sample a neural network and what the quality of random number generation should be. This research aimed to start addressing these two fundamental questions by examining basic “off the shelf” neural networks can be sampled through a few different mechanisms (including synapse “dropout” and neuron “dropout”) and examine how these sampling approaches can be evaluated both in terms of evaluating algorithm effectiveness and the required quality of random numbers.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2013

The Petrology and Geochemistry of Feldspathic Granulitic Breccia NWA 3163: Implications for the Lunar Crust

Lunar meteorites are crucial to understand the Moon s geological history because, being samples of the lunar crust that have been ejected by random impact events, they potentially originate from areas outside the small regions of the lunar surface sampled by the Apollo and Luna missions. The Apollo and Luna sample sites are contained within the Procellarum KREEP Terrain (PKT, Jolliff et al., 2000), where KREEP refers to potassium, rare earth element, and phosphorus-rich lithologies. The KREEP-rich rocks in the PKT are thought to be derived from late-stage residual liquids after approx.95-99% crystallization of a lunar magma ocean (LMO). These are understood to represent late-stage liquids which were enriched in incompatible trace elements (ITE) relative to older rocks (Snyder et al., 1992). As a consequence, the PKT is a significant reservoir for Th and KREEP. However, the majority of the lunar surface is likely to be significantly more depleted in ITE (84%, Jolliff et al., 2000). Lunar meteorites that are low in KREEP and Th may thus sample regions distinct from the PKT and are therefore a valuable source of information regarding the composition of KREEP-poor lunar crust. Northwest Africa (NWA) 3163 is a thermally metamorphosed ferroan, feldspathic, granulitic breccia composed of igneous clasts with a bulk anorthositic, noritic bulk composition. It is relatively mafic (approx.5.8 wt.% FeO; approx.5 wt.% MgO) and has some of the lowest concentrations of ITEs (17ppm Ba) compared to the feldspathic lunar meteorite (FLM) and Apollo sample suites (Hudgins et al., 2011). Localized plagioclase melting and incipient melting of mafic minerals require localized peak shock pressures in excess of 45 GPa (Chen and El Goresy, 2000; Hiesinger and Head, 2006). NWA 3163, and paired samples NWA 4481 and 4883, have previously been interpreted to represent an annealed micro-breccia which was produced by burial metamorphism at depth in the ancient lunar crust (Fernandes et al., 2009). This is in contrast to the interpretation of Hudgins et al. (2009) where NWA 3163 was interpreted to have formed through contact metamorphism. To further constrain its origin, we examine the petrogenesis of NWA 3163 with a particular emphasis on in-situ measurement of trace elements within constituent minerals, Sm-Nd and Rb-Sr isotopic systematics on separated mineral fractions and petrogenetic modeling.

McLeod, C. L.↗

NASA NTRS1990

Integrated Communication and Control Systems. III - Nonidentical sensor and controller sampling

Networking in Integrated Communication and Control Systems (ICCS) introduces randomly varying delays which degrade the system dynamic performance and are a source of potential instability. In Part I of this sequence of papers, a discrete-time, finite-dimensional model of the delayed control system for analysis and design of ICCS was developed where the sensor and controller have identical sampling rates. In Part II, two alternate approaches for ICCS design, namely, identical and nonidentical sampling rates for sensor and controller were proposed. This Part III presents extended modeling of ICCS for nonidentical sensor and controller sampling rates. This model is also suitable for analyzing tracking problems, i.e., control systems with time-dependent reference inputs.

Liou, Luen-Woei↗

NASA NTRS2012

Materials for Heated Head Automated Thermoplastic Tape Placement

NASA Langley Research Center (LaRC) is currently pursuing multiple paths to develop out of autoclave (OOA) polymeric composite materials and processes. Polymeric composite materials development includes the synthesis of new and/or modified thermosetting and thermoplastic matrix resins designed for specific OOA processes. OOA processes currently under investigation include vacuum bag only (VBO) prepreg/composite fabrication, resin transfer molding (RTM), vacuum assisted resin transfer molding (VARTM) and heated head automated thermoplastic tape placement (HHATP). This paper will discuss the NASA Langley HHATP facility and capabilities and recent work on characterizing thermoplastic tape quality and requirements for quality part production. Samples of three distinct versions of APC-2 (AS4/PEEK) thermoplastic dry tape were obtained from two materials vendors, TENCATE, Inc. and CYTEC Engineered Materials** (standard grade and an experimental batch). Random specimens were taken from each of these samples and subjected to photo-microscopy and surface profilometry. The CYTEC standard grade of APC-2 tape had the most voids and splits and the highest surface roughness and/or waviness. Since the APC-2 tape is composed of a thermoplastic matrix, it offers the flexibility of reprocessing to improve quality, and thereby improve final quality of HHATP laminates. Discussions will also include potential research areas and future work that is required to advance the state of the art in the HHATP process for composite fabrication.

Jensen, Brian J.↗

DOE OSTI2023

gLaSDI: Parametric physics-informed greedy latent space dynamics identification

A parametric adaptive physics-informed greedy Latent Space Dynamics Identification (gLaSDI) method is proposed for accurate, efficient, and robust data-driven reduced-order modeling of high-dimensional nonlinear dynamical systems. In the proposed gLaSDI framework, an autoencoder discovers intrinsic nonlinear latent representations of high-dimensional data, while dynamics identification (DI) models capture local latent-space dynamics. Here, an interactive training algorithm is adopted for the autoencoder and local DI models, which enables identification of simple latent-space dynamics and enhances accuracy and efficiency of data-driven reduced-order modeling. To maximize and accelerate the exploration of the parameter space for the optimal model performance, an adaptive greedy sampling algorithm integrated with a physics-informed residual-based error indicator and random-subset evaluation is introduced to search for the optimal training samples on the fly. Further, to exploit local latent-space dynamics captured by the local DI models for an improved modeling accuracy with a minimum number of local DI models in the parameter space, a -nearest neighbor convex interpolation scheme is employed. The effectiveness of the proposed framework is demonstrated by modeling various nonlinear dynamical problems, including Burgers equations, nonlinear heat conduction, and radial advection. The proposed adaptive greedy sampling outperforms the conventional predefined uniform sampling in terms of accuracy. Compared with the high-fidelity models, gLaSDI achieves 17 to 2,658× speed-up with 1 to 5% relative errors.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1995

A count probability cookbok: Spurious effects and the scaling model

We study the errors brought by finite volume effects and dilution effects on the practical determination of the count probability distribution function P(sub N)(n,l), which is the probability of having N objects in a cell of volume l cubed for a set of average number density n. Dilution effects are particularly revelant to the so-called sparse sampling strategy. This work is mainly done in the framework of the Bailan & Schaeffer scaling model, which assumes that the Q-body correlation functions obey the scaling relation Xi(sub Q)(lambda r(sub l),....lambda r(sub Q) = lambda(exp -(Q-1)gamma) Xi(sub Q)(r(sub 1),....r(sub Q)). We use three synthetic samples as references to perform our analysis: a fractal generated by a Rayleigh-Levy random walk with approximately 3 x 10(exp 4) objects, a sample dominated by a spherical power-law cluster with approximately 3 x 10(exp 4) objects and a cold dark matter (CDM) universe involving approximately 3 x 10(exp 5) matter particles.

Colombi, S.↗