Search NASA⌕ Search

SEARCH · Search NASA

Results for “clustering algorithms”

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 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

THE DEEP2 GALAXY REDSHIFT SURVEY: THE VORONOI-DELAUNAY METHOD CATALOG OF GALAXY GROUPS

We present a public catalog of galaxy groups constructed from the spectroscopic sample of galaxies in the fourth data release from the Deep Extragalactic Evolutionary Probe 2 (DEEP2) Galaxy Redshift Survey, including the Extended Groth Strip (EGS). The catalog contains 1165 groups with two or more members in the EGS over the redshift range 0 < z < 1.5 and 1295 groups at z > 0.6 in the rest of DEEP2. Twenty-five percent of EGS galaxies and fourteen percent of high-z DEEP2 galaxies are assigned to galaxy groups. The groups were detected using the Voronoi-Delaunay method (VDM) after it has been optimized on mock DEEP2 catalogs following similar methods to those employed in Gerke et al. In the optimization effort, we have taken particular care to ensure that the mock catalogs resemble the data as closely as possible, and we have fine-tuned our methods separately on mocks constructed for the EGS and the rest of DEEP2. We have also probed the effect of the assumed cosmology on our inferred group-finding efficiency by performing our optimization on three different mock catalogs with different background cosmologies, finding large differences in the group-finding success we can achieve for these different mocks. Using the mock catalog whose background cosmology is most consistent with current data, we estimate that the DEEP2 group catalog is 72% complete and 61% pure (74% and 67% for the EGS) and that the group finder correctly classifies 70% of galaxies that truly belong to groups, with an additional 46% of interloper galaxies contaminating the catalog (66% and 43% for the EGS). We also confirm that the VDM catalog reconstructs the abundance of galaxy groups with velocity dispersions above {approx}300 km s{sup -1} to an accuracy better than the sample variance, and this successful reconstruction is not strongly dependent on cosmology. This makes the DEEP2 group catalog a promising probe of the growth of cosmic structure that can potentially be used for cosmological tests.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Wang–Landau approach to the simulation of water clusters

The Wang–Landau Monte Carlo method [Wang FG, Landau DP. Efficient, multiple-range random walk algorithm to calculate the density of states. Phys Rev Lett. 2001;86:2050-2053; Determining the density of states for classical statistical models: a random walk algorithm to produce a flat histogram. Phys Rev E. 2001;64:056101] has been established as an effective computational approach for studying complex systems. In this study, we review the properties of 6 popular empirical water potentials, including a recently proposed explicit 3-body potential, obtained by applying the Wang–Landau method to the simulation of water clusters. Finally, comparing to numerous classical and first principle calculations, we show that several classical water models can provide a consistent description of the structural and thermodynamical properties of water clusters partially in agreement of ab initio results, although they were designed to reproduce macroscopic properties of water at ambient conditions.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2026

Degenerate coupled-cluster theory

A size-extensive, converging, black-box, ab initio coupled-cluster (ΔCC) ansatz is introduced that computes the energies and wave functions of states from any degenerate or nondegenerate Slater-determinant references with any numbers of α- and β-spin electrons, any patterns of orbital occupancy, any spin multiplicities, and any spatial symmetries. For a nondegenerate reference, it reduces to the single-reference coupled-cluster ansatz. For a degenerate multireference, it is a natural coupled-cluster extension of degenerate Møller–Plesset perturbation (ΔMP) theory. For ionized and electron-attached references, it is a coupled-cluster Green’s function, although the present theory is convergent toward the full-configuration-interaction limits, while the Feynman–Dyson many-body Green’s function (MBGF) theory generally is not. Its single-excitation instance is a projection Hartree–Fock theory as per the Thouless theorem, which may be useful for core ionizations, high-spin states, and possibly electron affinities. Additionally, a new multireference coupled-cluster theory for a general model space is developed. This quasidegenerate coupled-cluster (QCC) theory is exactly converging, but not black-box, and intended for strong correlation. Determinant-based, general-order algorithms of ΔCC and QCC theories are implemented and compared with configuration-interaction (CI) and equation-of-motion coupled-cluster (EOM-CC) theories through octuple excitations and with ΔMP and MBGF theories up to the nineteenth order. An algebraic, optimal-scaling algorithm of the ΔCC theory is computer-synthesized at the levels of single excitations (ΔCCS) and of single and double excitations (ΔCCSD). As a result, the order of performance is QCC ≈ ΔCC > EOM-CC > CI at the same order or QCC ≈ ΔCC > ΔMP > MBGF at the same cost scaling.

Hirata, So [University of Illinois at Urbana-Champ↗
DOE OSTI2015

Local time–space mesh refinement for simulation of elastic wave propagation in multi-scale media

This paper presents an original approach to local time–space grid refinement for the numerical simulation of wave propagation in models with localized clusters of micro-heterogeneities. The main features of the algorithm are –the application of temporal and spatial refinement on two different surfaces; –the use of the embedded-stencil technique for the refinement of grid step with respect to time; –the use of the Fast Fourier Transform (FFT)-based interpolation to couple variables for spatial mesh refinement. The latter makes it possible to perform filtration of high spatial frequencies, which provides stability in the proposed finite-difference schemes. In the present work, the technique is implemented for the finite-difference simulation of seismic wave propagation and the interaction of such waves with fluid-filled fractures and cavities of carbonate reservoirs. However, this approach is easy to adapt and/or combine with other numerical techniques, such as finite elements, discontinuous Galerkin method, or finite volumes used for approximation of various types of linear and nonlinear hyperbolic equations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Fe-Cluster Compounds of Chalcogenides: Candidates for Rare-Earth-Free Permanent Magnet and Magnetic Nodal-Line Topological Material

Here, Fe-cluster-based crystal structures are predicted for chalcogenides Fe 3 X 4 (X = S, Se, Te) using an adaptive genetic algorithm. Topologically different from the well-studied layered structures of iron chalcogenides, the newly predicted structures consist of Fe clusters that are either separated by the chalcogen atoms or connected via sharing of the vertex Fe atoms. Additionally, using first-principles calculations, we demonstrate that these structures have competitive or even lower formation energies than the experimentally synthesized Fe 3 X 4 compounds and exhibit interesting magnetic and electronic properties. In particular, we show that Fe 3 X 4 can be a good candidate as a rare-earth-free permanent magnet and Fe 3 X 4 can be a magnetic nodal-line topological material.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Efficient Ab Initio Auxiliary-Field Quantum Monte Carlo Calculations in Gaussian Bases via Low-Rank Tensor Decomposition

We describe an algorithm to reduce the cost of auxiliary-field quantum Monte Carlo (AFQMC) calculations for the electronic structure problem. The technique uses a nested low-rank factorization of the electron repulsion integral (ERI). While the cost of conventional AFQMC calculations in Gaussian bases scales as O(N 4 ), where N is the size of the basis, we show that ground-state energies can be computed through tensor decomposition with reduced memory requirements and subquartic scaling. Here, the algorithm is applied to hydrogen chains and square grids, water clusters, and hexagonal BN. In all cases, we observe significant memory savings and, for larger systems, reduced, subquartic simulation time.

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

Simulation and performance of an artificial retina for 40 MHz track reconstruction

We present the results of a detailed simulation of the artificial retina pattern-recognition algorithm, designed to reconstruct events with hundreds of charged-particle tracks in pixel and silicon detectors at LHCb with LHC crossing frequency of 40 MHz. Performances of the artificial retina algorithm are assessed using the official Monte Carlo samples of the LHCb experiment. We found performances for the retina pattern-recognition algorithm comparable with the full LHCb reconstruction algorithm.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Heterogeneous Graph Neural Network for identifying hadronically decayed tau leptons at the High Luminosity LHC

Here, we present a new algorithm that identifies reconstructed jets originating from hadronic decays of tau leptons against those from quarks or gluons. No tau lepton reconstruction algorithm is used. Instead, the algorithm represents jets as heterogeneous graphs with tracks and energy clusters as nodes and trains a Graph Neural Network to identify tau jets from other jets. Different attributed graph representations and different GNN architectures are explored. We propose to use differential track and energy cluster information as node features and a heterogeneous sequentially-biased encoding for the inputs to final graph-level classification.

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

Scalable Static and Dynamic Community Detection Using Grappolo

Graph clustering, popularly known as community detection, is a fundamental kernel for several applications of relevance to the Defense Advanced Research Projects Agency’s (DARPA) Hierarchical Identify Verify Exploit (HIVE) Pro- gram. Clusters or communities represent natural divisions within a network that are densely connected within a cluster and sparsely connected to the rest of the network. The need to compute clustering on large scale data necessitates the development of efficient algorithms that can exploit modern architectures that are fundamentally parallel in nature. How- ever, due to their irregular and inherently sequential nature, many of the current algorithms for community detection are challenging to parallelize. In response to the HIVE Graph Challenge, we present several parallelization heuristics for fast community detection using the Louvain method as the serial template. We implement all the heuristics in a software library called Grappolo. Using the inputs from the HIVE Challenge, we demonstrate superior performance and high quality solutions based on four parallelization heuristics. We use Grappolo on static graphs as the first step towards community detection on streaming graphs.

Community Detection↗
DOE OSTI2022

Defensive Islanding to Enhance the Resilience of Distribution Systems Against Cyber-Induced Failures

The extensive integration of communication, computation, and control technologies into cyber-physical power systems (CPPSs) has increased the vulnerabilities of CPPSs to cyberattacks. This calls for developing solutions that assess and reduce the impacts of cyber-induced failures on CPPSs. This paper proposes a defensive islanding strategy to isolate impacted parts of the CPPS and form self-sufficient islanded grids with an objective of minimum load curtailment. The defensive islanding aims to split a power system into smaller grids to improve its resilience against a potential extreme event. A clustering approach that leverages the hierarchical spectral clustering method is utilized for the optimal defensive islanding. The proposed approach captures the fragility behavior and loading conditions of power system components due to cyber-induced failures. A graphical-based coupling framework is used to map the impacts of cyber failures into operation of power system components. The proposed method is demonstrated on a modified 33-node distribution feeder system integrated with distributed energy resources. The amount of load curtailment and radiality constraints have been used to evaluate the performance of the proposed clustering strategies. The results show the capability of the proposed algorithm to create islands considering the cyber-induced failures for enhanced resilience.

cyber-induced failures↗
DOE OSTI2022

Distributed Hierarchical Contour Trees

Contour trees are a significant tool for data analysis as they capture both local and global variation. However, their utility has been limited by scalability, in particular for distributed computation and storage. We report a distributed data structure for storing the contour tree of a data set distributed on a cluster, based on a fan-in hierarchy, and an algorithm for computing it based on the boundary tree that represents only the superarcs of a contour tree that involve contours that cross boundaries between blocks. This allows us to limit the communication cost for contour tree computation to the complexity of the block boundaries rather than of the entire data set.

Carr, Hamish A↗
DOE OSTI2021

PHYSICS-BASED AUTOMATED REASONING FOR HEALTH MONITORING: SENSOR SET SELECTION

This paper addresses the problem of how to select a sensor set for equipment health monitoring that meets the needs of advanced O&M tasks that target cost reduction. They include maintenance optimization and asset management for the existing fleet and near-autonomous operation as currently envisioned for advanced reactors. The method uses physics-based automated reasoning to provide for a more “explainable” diagnosis. The algorithm is described along with its implementation on a computational cluster. Preliminary results for application to a use case in the current fleet are described.

diagnosis↗
NASA NTRS1975

M-DAS: System for multispectral data analysis

M-DAS is a ground data processing system designed for analysis of multispectral data. M-DAS operates on multispectral data from LANDSAT, S-192, M2S and other sources in CCT form. Interactive training by operator-investigators using a variable cursor on a color display was used to derive optimum processing coefficients and data on cluster separability. An advanced multivariate normal-maximum likelihood processing algorithm was used to produce output in various formats: color-coded film images, geometrically corrected map overlays, moving displays of scene sections, coverage tabulations and categorized CCTs. The analysis procedure for M-DAS involves three phases: (1) screening and training, (2) analysis of training data to compute performance predictions and processing coefficients, and (3) processing of multichannel input data into categorized results. Typical M-DAS applications involve iteration between each of these phases. A series of photographs of the M-DAS display are used to illustrate M-DAS operation.

Johnson, R. H.↗
NASA NTRS2012

Particle Filtering for Model-Based Anomaly Detection in Sensor Networks

A novel technique has been developed for anomaly detection of rocket engine test stand (RETS) data. The objective was to develop a system that postprocesses a csv file containing the sensor readings and activities (time-series) from a rocket engine test, and detects any anomalies that might have occurred during the test. The output consists of the names of the sensors that show anomalous behavior, and the start and end time of each anomaly. In order to reduce the involvement of domain experts significantly, several data-driven approaches have been proposed where models are automatically acquired from the data, thus bypassing the cost and effort of building system models. Many supervised learning methods can efficiently learn operational and fault models, given large amounts of both nominal and fault data. However, for domains such as RETS data, the amount of anomalous data that is actually available is relatively small, making most supervised learning methods rather ineffective, and in general met with limited success in anomaly detection. The fundamental problem with existing approaches is that they assume that the data are iid, i.e., independent and identically distributed, which is violated in typical RETS data. None of these techniques naturally exploit the temporal information inherent in time series data from the sensor networks. There are correlations among the sensor readings, not only at the same time, but also across time. However, these approaches have not explicitly identified and exploited such correlations. Given these limitations of model-free methods, there has been renewed interest in model-based methods, specifically graphical methods that explicitly reason temporally. The Gaussian Mixture Model (GMM) in a Linear Dynamic System approach assumes that the multi-dimensional test data is a mixture of multi-variate Gaussians, and fits a given number of Gaussian clusters with the help of the wellknown Expectation Maximization (EM) algorithm. The parameters thus learned are used for calculating the joint distribution of the observations. However, this GMM assumption is essentially an approximation and signals the potential viability of non-parametric density estimators. This is the key idea underlying the new approach.

Solano, Wanda↗
DOE OSTI2015

Translating Non-Trivial Algorithms from the Circuit Model to the Measurement

We provide a set of prescriptions for implementing a circuit model algorithm as measurement based quantum computing via a large discrete cluster state constructed sequentially, from qubits implemented as single photons. We describe a large optical discrete graph state capable of searching logical 4 and 8 element lists as an example. To do so we have developed several prescriptions based on analytic evaluation of the evolution of discrete cluster states and graph state equations. We describe the cluster state as a sequence of repeated entanglement and measurement steps using a small number of single photons for each step. These prescriptions can be generalized to implement any logical circuit model operation with appropriate single photon measurements and feed forward error corrections. Such a cluster state is not guaranteed to be optimal (i.e. minimum number of photons, measurements, run time).

Smith, IV, Amos M↗
DOE OSTI2011

Toward IMRT 2D dose modeling using artificial neural networks: A feasibility study

Purpose: To investigate the feasibility of artificial neural networks (ANN) to reconstruct dose maps for intensity modulated radiation treatment (IMRT) fields compared with those of the treatment planning system (TPS). Methods: An artificial feed forward neural network and the back-propagation learning algorithm have been used to replicate dose calculations of IMRT fields obtained from PINNACLE{sup 3} v9.0. The ANN was trained with fluence and dose maps of IMRT fields for 6 MV x-rays, which were obtained from the amorphous silicon (a-Si) electronic portal imaging device of Novalis TX. Those fluence distributions were imported to the TPS and the dose maps were calculated on the horizontal midpoint plane of a water equivalent homogeneous cylindrical virtual phantom. Each exported 2D dose distribution from the TPS was classified into two clusters of high and low dose regions, respectively, based on the K-means algorithm and the Euclidian metric in the fluence-dose domain. The data of each cluster were divided into two sets for the training and validation phase of the ANN, respectively. After the completion of the ANN training phase, 2D dose maps were reconstructed by the ANN and isodose distributions were created. The dose maps reconstructed by ANN were evaluated and compared with the TPS, where the mean absolute deviation of the dose and the {gamma}-index were used. Results: A good agreement between the doses calculated from the TPS and the trained ANN was achieved. In particular, an average relative dosimetric difference of 4.6% and an average {gamma}-index passing rate of 93% were obtained for low dose regions, and a dosimetric difference of 2.3% and an average {gamma}-index passing rate of 97% for high dose region. Conclusions: An artificial neural network has been developed to convert fluence maps to corresponding dose maps. The feasibility and potential of an artificial neural network to replicate complex convolution kernels in the TPS for IMRT dose calculations have been demonstrated.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2020

Elucidation of Bottom-up Growth of CaCO3 Involving Prenucleation Clusters from Structure Predictions and Decomposition of Globally Optimized (CaCO3)n Nanoclusters

Low-energy minima structures for (CaCO3)n, n = 28, are predicted using bottom-up genetic algorithms in conjunction with density functional theory electronic structure calculations, in comparison with the frozen and relaxed top-down clusters generated by cuts from the calcite, vaterite, and aragonite crystal structures. Similarities in structural motifs for the bottom-up and relaxed top-down are revealed using a fragment recognition technique. Fragment energy decomposition analysis shows that the bottom-up and relaxed top-down clusters belong to two classes of amorphous clusters with distinct intracluster energy distributions, despite their structural similarity. The bottom-up clusters with >20 formula units are surface stabilized with negative surface energy densities. In contrast, the top-down clusters are interior stabilized with positive surface energy densities. We prove that the sign of the surface energy density determines whether the nucleation reaction energy as a function of nuclear size has a maximum or a minimum. The surface-stabilized bottom-up clusters are proposed to be a type of prenucleation cluster at the minimum of the nucleation reaction energy. A mechanism for mineralization of CaCO3 involving prenucleation clusters and nonclassical growth pathway is proposed on the basis of our theoretical findings, which is consistent with previous titration experiments.

calcium carbonate, prenucleation cluster, biominer↗
DOE OSTI2014

Approximate Hessian for accelerating ab initio structure relaxation by force fitting

We present a method to approximate the Hessian matrix of the Born-Oppenheimer energy landscape by using a simple force field model whose parameters are fitted to on-the-flight ab initio results. The inversed Hessian matrix is used as the preconditioner of conjugate gradient algorithms to speed up the atomic structure relaxation, resulting in a speedup factor of 2 to 5 on systems of bulk, slab, sheets, and atomic clusters. Because the force field model employed is simple and general, the parameter fitting is straightforward; the method is applicable to a variety of complicated systems for minimum structure relaxation. In the metal cluster new structure search, the new method yields better structures than the one obtained before with conventional algorithms.

36 MATERIALS SCIENCE↗