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Results for “purely high dimensional”

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.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Foundational Principles for Large-Scale Inference: Illustrations Through Correlation Mining

When can reliable inference be drawn in the ‘‘Big Data’’ context? This article presents a framework for answering this fundamental question in the context of correlation mining, with implications for general large-scale inference. In large-scale data applications like genomics, connectomics, and eco-informatics, the data set is often variable rich but sample starved: a regime where the number n of acquired samples (statistical replicates) is far fewer than the number p of observed variables (genes, neurons, voxels, or chemical constituents). Much of recent work has focused on understanding the computational complexity of proposed methods for ‘‘Big Data.’’ Sample complexity, however, has received relatively less attention, especially in the setting when the sample size n is fixed, and the dimension p grows without bound. To address this gap, we develop a unified statistical framework that explicitly quantifies the sample complexity of various inferential tasks. Sampling regimes can be divided into several categories: 1) the classical asymptotic regime where the variable dimension is fixed and the sample size goes to infinity; 2) the mixed asymptotic regime where both variable dimension and sample size go to infinity at comparable rates; and 3) the purely high-dimensional asymptotic regime where the variable dimension goes to infinity and the sample size is fixed. Each regime has its niche but only the latter regime applies to exa-scale data dimension. We illustrate this high-dimensional framework for the problem of correlation mining, where it is the matrix of pairwise and partial correlations among the variables that are of interest. Correlation mining arises in numerous applications and subsumes the regression context as a special case. We demonstrate various regimes of correlation mining based on the unifying perspective of high-dimensional learning rates and sample complexity for different structured covariance models and different inference tasks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Quickest Detection for Changes in Maximal kNN Coherence of Random Matrices

This paper addresses the problem of quickest detection of a change in the maximal coherence between columns of a n p random matrix based on a sequence of matrix observations having a single unknown change point. The random matrix is assumed to have identically distributed rows and the maximal coherence is defined as the largest of the ( 2 p) correlation coefficients associated with any row. Likewise the κ nearest neighbor (kNN) coherence is defined as the κ-th largest of these correlation coefficients. The forms of the preand postchange distributions of the observed matrices are assumed to belong to the family of elliptically contoured densities with sparse dispersion matrices but are otherwise unknown. A nonparametric stopping rule is proposed that is based on the maximal kNN sample coherence between columns of each observed random matrix. This is a summary statistic that is related to a test of existence of a hub vertex in a sample correlation graph having degree at least κ. Performance bounds on the delay and false alarm performance of the proposed stopping rule are obtained in the purely high-dimensional regime where p → ∞ and n is fixed. When the prechange dispersion matrix is diagonal it is shown that, among all functions of the proposed summary statistic, the proposed stopping rule is asymptotically optimal under a minimax quickest change detection model as the stopping threshold approaches infinity. Furthermore, the theory developed also applies to sequential hypothesis testing and fixed sample size tests.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS1989

Airfoil profile drag increase due to acoustic excitation

A two-dimensional airfoil (NACA-0009) is subjected to high intensity pure-tone sound over a 1-5 kHz frequency range while immersed in a flow with 240 ft/sec velocity in a quiet flow facility with a Reynolds number of 3 million. Wake dynamic pressures are determined, and the momentum deficit is used to calculate a two-dimensional drag coefficient. Significant increases in drag are observed when the airfoil is subjected to high-intensity sound at critical frequencies. The increased drag is accompanied by movement of the natural transition location. When the transition is fixed by roughness at 10 percent chord, no further transition movement is observed in response to an acoustic Tollmien-Schlichting disturbance. However, a 4 percent increase in the sectional drag coefficient is noted. It is believed to be due to the sound exciting the flow near the airfoil surface (shear layer), thus causing the existing turbulence to become more intense, possess a higher mixing rate (momentum), and increase the skin friction.

Shearin, John G.↗
DOE OSTI2025

TensorID v1.0

This Python software package includes new and efficient algorithms for satellite and core interpolative decomposition of tensor data. In general, these algorithms target high-dimensional data reduction and compression. The software is purely numerical and can be applied by others to many important sources of tensor data generated by computation or experiment.

Zhang, Yifan [Lawrence Berkeley National Laborator↗
NASA NTRS1987

Acoustic effects on profile drag of a laminar flow airfoil

A two-dimensional laminar flow airfoil (NLF-0414) was subjected to high-intensity sound (pure tones and white noise) over a frequency range of 2 to 5 kHz, while immersed in a flow of 240 ft/sec (Rn of 3 million) in a quiet flow facility. Using a wake-rake, wake dynamic pressures were determined and the deficit in momentum was used to calculate a two dimensional drag coefficient. Significant increases in drag were observed when the airfoil was subjected to the high intensity sound at critical sound frequencies. However, the increased drag was not accompanied by movement of the transition location.

Shearin, John G.↗
DOE OSTI2018

Multidimensional spectroscopy with attosecond extreme ultraviolet and shaped near-infrared pulses

Dynamics following excitation with attosecond extreme ultraviolet (XUV) pulses arise from enormous numbers of accessible excited states, complicating the retrieval of state-specific time evolutions. We develop attosecond XUV multidimensional spectroscopy here to separate interfering pathways on a near-infrared (NIR) energy axis, retrieving single state dynamics in argon atoms in a two-dimensional (2D) XUV-NIR spectrum. In this experiment, we measure four-wave mixing signal arising from the interaction of XUV attosecond pulses centered around 15 eV with two few-cycle NIR pulses. The 2D spectrum is created by measuring the emitted XUV signal field spectrum while applying narrowband amplitude and phase modulations to one of the NIR pulses. Application of such a technique to systems of high dimensionality will provide for the observation of state-resolved pure electronic dynamics, in direct analogy to phenomena unraveled by multidimensional spectroscopies at optical frequencies.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Latent-space time evolution of non-intrusive reduced-order models using Gaussian process emulation

Non-intrusive reduced-order models (ROMs) have recently generated considerable interest for constructing computationally efficient counterparts of nonlinear dynamical systems emerging from various domain sciences. They provide a low-dimensional emulation framework for systems that may be intrinsically high-dimensional. This is accomplished by utilizing a construction algorithm that is purely data-driven. It is no surprise, therefore, that the algorithmic advances of machine learning have led to non-intrusive ROMs with greater accuracy and computational gains. However, in bypassing the utilization of an equation-based evolution, it is often seen that the interpretability of the ROM framework suffers. This becomes more problematic when black-box deep learning methods are used which are notorious for lacking robustness outside the physical regime of the observed data. In this article, we propose the use of a novel latent-space interpolation algorithm based on Gaussian process regression. Notably, this reduced-order evolution of the system is parameterized by control parameters to allow for interpolation in space. The use of this procedure also allows for a continuous interpretation of time which allows for temporal interpolation. The latter aspect provides information, with quantified uncertainty, about full-state evolution at a finer resolution than that utilized for training the ROMs. This research assesses the viability of this algorithm for an advection-dominated system given by the inviscid shallow water equations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Three-dimensional morphology of an ultrafine Al-Si eutectic produced via laser rapid solidification

Al-Si alloys processed by laser rapid solidification yield eutectic microstructures with ultrafine and interconnected fibers. Such fibrous structures have long been thought to bear resemblance to those formed in impurity-doped alloys upon conventional casting. Here, we show that any similarity is purely superficial. By harnessing high-throughput characterization and computer vision techniques, we perform a three-dimensional analysis of the branching behavior of the ultrafine eutectic and compare it against an impurity-modified eutectic as well as a random fractal (as a benchmark). Differences in the branching statistics point to different microstructural origins of the impurity- and quench-modified eutectic. Finally, our quantitative approach is not limited to the data presented here but can be used to extract abstract information from other volumetric datasets, without customization.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Knowledge-guided learning with curated prior genetic biomarkers for robust model interpretation

Abstract Motivation Knowledge-guided learning offers effective and robust model training strategies in data-scarce settings by incorporating established domain knowledge, thereby enhancing generalization, robustness, and interpretability. By contrast, conventional deep learning approaches rely purely on data-driven learning, which can limit robust model interpretability, particularly in high-dimensional settings with limited size samples. In computational biology, knowledge-guided learning has primarily leveraged network- and structural-based knowledge, leading to biologically interpretable representations and enhanced predictive performance compared to conventional approaches. However, curated biomarkers, one of the most accessible forms of biological knowledge, remain largely unexplored within knowledge-guided paradigms. Results In this study, we propose a model-agnostic training paradigm, Biomarker-driven Explainable Prior-guided Learning (BioExPL), that can be applied to any neural networks that incorporates curated prior knowledge. BioExPL enforces neural networks to reflect curated biomarker priors in their latent representations through a novel knowledge-alignment loss. BioExPL consistently demonstrated significantly improved predictive performance and enhanced model interpretability with minimized computational overhead in simulation studies and intensive experiments on multiple cancer datasets. BioExPL not only integrates prior curated knowledge into the model but also accurately identifies unknown associated signals additionally. BioExPL is model-agnostic and domain-independent, enabling its integration into diverse neural network architectures. Availability and implementation The open-source is publicly available at: https://github.com/datax-lab/BioExPL.

Baek, Beomsu [Department of Computer Science, Univ↗
DOE OSTI2016

Multiparameter spatio-thermochemical probing of flame–wall interactions advanced with coherent Raman imaging

Ultrabroadband coherent anti-Stokes Ra man spectroscopy (CARS) has been developed for one -dimensional imaging of temperature and major species distributions simultaneously in the near-wall region of a methane/air flame supported on a side-wall-quenching (SWQ) burner. Automatic temporal and spatial overlap of the ~7 femtosecond pump and Stokes pulses is achieved utilizing a two-beam CARS phase-matching scheme, and the crossed ~75 picosecond probe beam provide s excellent spatial sectioning of the probed location. Concurrent detection of N 2 , O 2 , H 2 , CO, CO 2 , and CH 4 is demonstrated while high-fidelity flame thermometry is assessed from the N 2 pure rotational S-branch in a one-dimensional -CARS imaging configuration. A methane/air premixed flame at lean, stoichiometric, and rich conditions ( Φ = 0.83, 1.0 , and 1.2) and Reynolds number = 5,000 is probed as it quenches against a cooled steel side- wall parallel to the flow providing a persistent flame-wall interaction. Here, an imaging resolution of better than 40 μm is achieved across the field -of-view, thus allowing thermochemical states (temperature and major species) of the thermal boundary layer to be resolved to within ~30 μm of the interface.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Dry efficient cleaning of poly-methyl-methacrylate residues from graphene with high-density H{sub 2} and H{sub 2}-N{sub 2} plasmas

Graphene is the first engineering electronic material, which is purely two-dimensional: it consists of two exposed sp{sup 2}-hybridized carbon surfaces and has no bulk. Therefore, surface effects such as contamination by adsorbed polymer residues have a critical influence on its electrical properties and can drastically hamper its widespread use in devices fabrication. These contaminants, originating from mandatory technological processes of graphene synthesis and transfer, also impact fundamental studies of the electronic and structural properties at the atomic scale. Therefore, graphene-based technology and research requires “soft” and selective surface cleaning techniques dedicated to limit or to suppress this surface contamination. Here, we show that a high-density H{sub 2} and H{sub 2}-N{sub 2} plasmas can be used to selectively remove polymeric residues from monolayer graphene without any damage on the graphene surface. The efficiency of this dry-cleaning process is evidenced unambiguously by a set of spectroscopic and microscopic methods, providing unprecedented insights on the cleaning mechanisms and highlighting the role of specific poly-methyl-methacrylate residues at the graphene interface. The plasma is shown to perform much better cleaning than solvents and has the advantage to be an industrially mature technology adapted to large area substrates. The process is transferable to other kinds of two-dimensional material and heterostructures.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Probing multi-dimensional composition spaces in search of strong metallic alloys

Refractory complex concentrated alloys (RCCA) offer exceptionally high-temperature strength compared to pure metals and dilute alloys, but predictive theory for RCCA design is lacking. We present large-scale molecular Dynamics (MD) simulations of crystal plasticity to explore alloy compositions for maximum mechanical strength, focusing on Fe-Ta-W and Nb-Ta-Mo-W alloy families modeled with Embedded Atom Model (EAM) and Spectral Neighbor Analysis Potentials (SNAP). To efficiently guide the search for strong alloy compositions, we employ iterative optimization using Gaussian process regression. Many simulated RCCA compositions exhibit pronounced cocktail strengthening, with strengths surpassing their strongest constituent metal, tungsten. Contrary to expectations, the highest strength is found on binary edges of the RCCA composition space. Detailed analyses of atomistic simulations reveal that, similar to pure BCC metals, plastic response in RCCA is primarily governed by screw dislocations. However, at large strains, dislocation multiplication and interactions (Taylor hardening) become the dominant mechanisms contributing to RCCA strength.

Materials science↗
DOE OSTI2025

Moment-based adaptive time integration for thermal radiation transport

Here, in this paper we develop a framework for moment-based adaptive time integration of deterministic multifrequency thermal radiation transpot (TRT). We generalize our recent semi-implicit-explicit (IMEX) integration framework for gray TRT to multifrequency TRT, and also introduce a semi-implicit variation that facilitates higher-order integration of TRT, where each stage is implicit in all components except opacities. To appeal to the broad literature on adaptivity with Runge–Kutta methods, we derive new embedded methods for four asymptotic preserving IMEX Runge–Kutta schemes we have found to be robust in our previous work on TRT and radiation hydrodynamics. We then use a moment-based high-order-low-order representation of the transport equations. Due to the high dimensionality, memory is always a concern in simulating TRT. We form error estimates and adaptivity in time purely based on temperature and radiation energy, for a trivial overhead in computational cost and memory usage compared with the base second order integrators. We then test the adaptivity in time on the tophat and Larsen problem, demonstrating the ability of the adaptive algorithm to naturally vary the timestep across 4–5 orders of magnitude, ranging from the dynamical timescales of the streaming regime to the thick diffusion limit.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Surface-assisted DNA self-assembly: An enzyme-free strategy towards formation of branched DNA lattice

DNA based self-assembled nanostructures and DNA origami has proven useful for organizing nanomaterials with firm precision. However, for advanced applications like nanoelectronics and photonics, large-scale organization of self-assembled branched DNA (bDNA) into periodic lattices is desired. In this communication for the first time we report a facile method of self-assembly of Y-shaped bDNA nanostructures on the cationic surface of Aluminum (Al) foil to prepare periodic two dimensional (2D) bDNA lattice. Particularly those Y-shaped bDNA structures having smaller overhangs and unable to self-assemble in solution, they are easily assembled on the surface of Al foil in the absence of ligase. Field emission scanning electron microscopy (FESEM) analysis shows homogenous distribution of two-dimensional bDNA lattices across the Al foil. When the assembled bDNA structures were recovered from the Al foil and electrophoresed in nPAGE only higher order polymeric bDNA structures were observed without a trace of monomeric structures which confirms the stability and high yield of the bDNA lattices. Therefore, this enzyme-free economic and efficient strategy for developing bDNA lattices can be utilized in assembling various nanomaterials for functional molecular components towards development of DNA based self-assembled nanodevices. - Highlights: • Al foil surface-assisted self-assembly of monomeric structures into larger branched DNA lattice. • FESEM study confirms the uniform distribution of two-dimensional bDNA lattice structures across the surface of Al foil. • Enzyme-free and economic strategy to prepare higher order structures from simpler DNA nanostructures have been confirmed by recovery assay. • Use of well proven sequences for the preparation of pure Y-shaped monomeric DNA nanostructure with high yield.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Observation of highly dispersive bands in pure thin film C 60

While long-theorized, the direct observation of multiple highly dispersive ${\mathrm{C}}_{60}$ valence bands has eluded researchers for more than two decades due to a variety of intrinsic and extrinsic factors. We report a realization of multiple highly dispersive (330–520 meV) valence bands in pure thin film ${\mathrm{C}}_{60}$ on a novel substrate—the three-dimensional topological insulator ${\mathrm{Bi}}_{2}{\mathrm{Se}}_{3}$—through the use of angle-resolved photoemission spectroscopy (ARPES) and first-principles calculations. The effects of this novel substrate reducing ${\mathrm{C}}_{60}$ rotational disorder are discussed. Our results provide important considerations for past and future band structure studies as well as the increasingly popular ${\mathrm{C}}_{60}$ electronic device applications, especially those making use of heterostructures.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Purely rotational symmetry-protected topological crystalline insulator α -Bi 4 Br 4

Abstract Rotational-symmetry-protected topological crystalline insulators (TCIs) are expected to host unique boundary modes, in that the surface normal to the rotational axis can feature surface states with ‘unpinned’ Dirac points, which are not constrained to lie on high symmetry points or lines, but can lie at any general k point in the Brillouin zone. Also, as a higher order bulk boundary correspondence is involved here, a three-dimensional (3D) TCI can support one-dimensional (1D) helical edge states. Using first-principles band structure calculations, we identify the van der Waals material -Bi 4 Br 4 as a purely rotation symmetry protected TCI. We show that the surface of Bi 4 Br 4 exhibits a pair of unpinned topological Dirac fermions which are related to the presence of a two-fold rotation axis. These unpinned Dirac fermions possess an exotic spin texture which will be highly favorable for spin transport, and a band structure that consists of van Hove singularities due to a Lifshitz transition. We also identify 1D topological hinge states along the edges of an -Bi 4 Br 4 rod. We comment on how the predicted topological features in -Bi 4 Br 4 could be accessed experimentally.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

Coherent optical transients observed in rubidium atomic line filtered Doppler velocimetry experiments

We report the first successful results from our novel Rubidium Atomic Line Filtered (RALF) Doppler velocimetry apparatus, along with unanticipated oscillatory signals due to coherent optical transients generated within pure Rb vapor cells. RALF is a high-velocity and high-acceleration extension of the well-known Doppler Global Velocimetry (DGV) technique for constructing multi-dimensional flow velocity vector maps in aerodynamics experiments [H. Komine, U.S. Patent No. 4,919,536 (24 April 1990)]. RALF exploits the frequency dependence of pressure-broadened Rb atom optical absorptions in a heated Rb/N{sub 2} gas cell to encode the Doppler shift of reflected near-resonant (λ{sub 0} ≈ 780.24 nm) laser light onto the intensity transmitted by the cell. The present RALF apparatus combines fiber optic and free-space components and was built to determine suitable operating conditions and performance parameters for the Rb/N{sub 2} gas cells. It yields single-spot velocities of thin laser-driven-flyer test surfaces and incorporates a simultaneous Photonic Doppler Velocimetry (PDV) channel [Strand et al., Rev. Sci. Instrum. 77, 083108 (2006)] for validation of the RALF results, which we demonstrate here over the v = 0 to 1 km/s range. Both RALF and DGV presume the vapor cells to be simple Beer's Law optical absorbers, so we were quite surprised to observe oscillatory signals in experiments employing low pressure pure Rb vapor cells. We interpret these oscillations as interference between the Doppler shifted reflected light and the Free Induction Decay (FID) coherent optical transient produced within the pure Rb cells at the original laser frequency; this is confirmed by direct comparison of the PDV and FID signals. We attribute the different behaviors of the Rb/N{sub 2} vs. Rb gas cells to efficient dephasing of the atomic/optical coherences by Rb-N{sub 2} collisions. The minimum necessary N{sub 2} buffer gas density ≈0.3 amagat translates into a smallest useful velocity range of 0 to 2 km/s, which can readily be extended to cover the 0 to 10 km/s range, and beyond. The recognition that coherent optical transients can be produced within low pressure vapor cells during velocimetry experiments may offer new insights into some quantitative discrepancies reported in earlier DGV studies. Future plans include “line-RALF” experiments with streak camera detection, and two-dimensional surface velocity mapping using pulsed laser illumination and/or gated intensified CCD camera detection.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗