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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
NASA NTRS1982

Out-of-Core Solutions of Complex Sparse Linear Equations

ETCLIB is library of subroutines for obtaining out-of-core solutions of complex sparse linear equations. Routines apply to dense and sparse matrices too large to be stored in core. Useful for solving any set of linear equations, but particularly useful in cases where coefficient matrix has no special properties that guarantee convergence with any of interative processes. The only assumption made is that coefficient matrix is not singular.

Yip, E. L.↗
NASA NTRS1985

A General-applications Direct Global Matrix Algorithm for Rapid Seismo-acoustic Wavefield Computations

A new matrix method for rapid wave propagation modeling in generalized stratified media, which has recently been applied to numerical simulations in diverse areas of underwater acoustics, solid earth seismology, and nondestructive ultrasonic scattering is explained and illustrated. A portion of recent efforts jointly undertaken at NATOSACLANT and NORDA Numerical Modeling groups in developing, implementing, and testing a new fast general-applications wave propagation algorithm, SAFARI, formulated at SACLANT is summarized. The present general-applications SAFARI program uses a Direct Global Matrix Approach to multilayer Green's function calculation. A rapid and unconditionally stable solution is readily obtained via simple Gaussian ellimination on the resulting sparsely banded block system, precisely analogous to that arising in the Finite Element Method. The resulting gains in accuracy and computational speed allow consideration of much larger multilayered air/ocean/Earth/engineering material media models, for many more source-receiver configurations than previously possible. The validity and versatility of the SAFARI-DGM method is demonstrated by reviewing three practical examples of engineering interest, drawn from ocean acoustics, engineering seismology and ultrasonic scattering.

Schmidt, H.↗
NASA NTRS1989

Solving sparse triangular linear systems on parallel computers

This paper describes and compares three parallel algorithms for solving sparse triangular systems of equations. These methods involve some preprocessing overhead and are primarily of interest in solving many systems with the same coefficient matrix. The first approach is to use a fixed blocksize and form the inverse of the diagonal blocks. The second approach is to use a variable blocksize and reorder the unknowns so that the diagonal blocks are diagonal matrices. The latter technique is called level scheduling because of how it is represented in the adjacency graph, and both row-wise and jagged diagonal storage for the off-diagonal blocks are considered. These techniques are analyzed for general parallel computers and experiments are presented for the eight-processor Alliant FX/8.

Anderson, Edward↗
NASA NTRS2011

Efficient Kriging Algorithms

More efficient versions of an interpolation method, called kriging, have been introduced in order to reduce its traditionally high computational cost. Written in C++, these approaches were tested on both synthetic and real data. Kriging is a best unbiased linear estimator and suitable for interpolation of scattered data points. Kriging has long been used in the geostatistic and mining communities, but is now being researched for use in the image fusion of remotely sensed data. This allows a combination of data from various locations to be used to fill in any missing data from any single location. To arrive at the faster algorithms, sparse SYMMLQ iterative solver, covariance tapering, Fast Multipole Methods (FMM), and nearest neighbor searching techniques were used. These implementations were used when the coefficient matrix in the linear system is symmetric, but not necessarily positive-definite.

Memarsadeghi, Nargess↗
DOE OSTI2021

On the Strong Convergence of Forward-Backward Splitting in Reconstructing Jointly Sparse Signals

We consider the problem of reconstructing an infinite set of sparse, finite-dimensional vectors, that share a common sparsity pattern, from incomplete measurements. This is in contrast to the work (Daubechies et al., Pure Appl. Math. 57(11), 1413–1457, 2004), where the single vector signal can be infinite-dimensional, and (Fornasier and Rauhut, SIAM J. Numer. Anal. 46(2), 577613, 2008), which extends the aforementioned work to the joint sparse recovery of finite number of infinite-dimensional vectors. In our case, to take account of the joint sparsity and promote the coupling of nonvanishing components, we employ a convex relaxation approach with mixed norm penalty ℓ 2,1 . This paper discusses the computation of the solutions of linear inverse problems with such relaxation by a forward-backward splitting algorithm. However, since the solution matrix possesses infinitely many columns, the arguments of Daubechies et al. (Pure Appl. Math. 57(11), 1413–1457, 2004) no longer apply. As such, we establish new strong convergence results for the algorithm, in particular when the set of jointly sparse vectors is infinite.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2000

Quantitative image analysis of laminin immunoreactivity in skin basement membrane irradiated with 1 GeV/nucleon iron particles

We previously reported that laminin immunoreactivity in mouse mammary epithelium is altered shortly after whole-body irradiation with 0.8 Gy from 600 MeV/nucleon iron ions but is unaffected after exposure to sparsely ionizing radiation. This observation led us to propose that the effect could be due to protein damage from the high ionization density of the ion tracks. If so, we predicted that it would be evident soon after radiation exposure in basement membranes of other tissues and would depend on ion fluence. To test this hypothesis, we used immunofluorescence, confocal laser scanning microscopy, and image segmentation techniques to quantify changes in the basement membrane of mouse skin epidermis. At 1 h after exposure to 1 GeV/nucleon iron ions with doses from 0.03 to 1.6 Gy, neither the visual appearance nor the mean pixel intensity of laminin in the basement membrane of mouse dorsal skin epidermis was altered compared to sham-irradiated tissue. This result does not support the hypothesis that particle traversal directly affects laminin protein integrity. However, the mean pixel intensity of laminin immunoreactivity was significantly decreased in epidermal basement membrane at 48 and 96 h after exposure to 0.8 Gy 1 GeV/nucleon iron ions. We confirmed this effect with two additional antibodies raised against affinity-purified laminin 1 and the E3 fragment of the long-arm of laminin 1. In contrast, collagen type IV, another component of the basement membrane, was unaffected. Our studies demonstrate quantitatively that densely ionizing radiation elicits changes in skin microenvironments distinct from those induced by sparsely ionizing radiation. Such effects may might contribute to the carcinogenic potential of densely ionizing radiation by altering cellular signaling cascades mediated by cell-extracellular matrix interactions.

Non-NASA Center↗
DOE OSTI2022

Large-Scale Optimization with Linear Equality Constraints Using Reduced Compact Representation

For optimization problems with linear equality constraints, we prove that the (1,1) block of the inverse KKT matrix remains unchanged when projected onto the nullspace of the constraint matrix. In this work, we develop reduced compact representations of the limited-memory inverse BFGS Hessian to compute search directions efficiently when the constraint Jacobian is sparse. Orthogonal projections are implemented by a sparse QR factorization or a preconditioned LSQR iteration. In numerical experiments two proposed trust-region algorithms improve in computation times, often significantly, compared to previous implementations of related algorithms and compared to IPOPT.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1990

Application of symbolic computations to the constitutive modeling of structural materials

In applications involving elevated temperatures, the derivation of mathematical expressions (constitutive equations) describing the material behavior can be quite time consuming, involved and error-prone. Therefore intelligent application of symbolic systems to faciliate this tedious process can be of significant benefit. Presented here is a problem oriented, self contained symbolic expert system, named SDICE, which is capable of efficiently deriving potential based constitutive models in analytical form. This package, running under DOE MACSYMA, has the following features: (1) potential differentiation (chain rule), (2) tensor computations (utilizing index notation) including both algebraic and calculus; (3) efficient solution of sparse systems of equations; (4) automatic expression substitution and simplification; (5) back substitution of invariant and tensorial relations; (6) the ability to form the Jacobian and Hessian matrix; and (7) a relational data base. Limited aspects of invariant theory were also incorporated into SDICE due to the utilization of potentials as a starting point and the desire for these potentials to be frame invariant (objective). The uniqueness of SDICE resides in its ability to manipulate expressions in a general yet pre-defined order and simplify expressions so as to limit expression growth. Results are displayed, when applicable, utilizing index notation. SDICE was designed to aid and complement the human constitutive model developer. A number of examples are utilized to illustrate the various features contained within SDICE. It is expected that this symbolic package can and will provide a significant incentive to the development of new constitutive theories.

Arnold, Steven M.↗
DOE OSTI2024

Exploration with Scalable Gaussian Process Reinforcement Learning

Exploration is a challenging problem in reinforcement learning (RL), especially in environments with sparse rewards. Quantifying and utilizing the parametric uncertainty has been shown to be paramount for successful exploration [Osband et al., 2018]. Bayesian, or approximately Bayesian, methods present a principled means of estimating the parametric uncertainty in RL problems. Gaussian processes, nonparametric Bayesian models, are often impractical due to poor scalability and computational bottlenecks. We introduce a scalable Gaussian process RL (GPRL) method which directly induces sparsity in the covariance matrix to facilitate faster computation. This is a departure from previous GPRL methods which instead rely on data reduction and subsampling. We compare various covariance-based exploration techniques (Thompson sampling, upper confidence bound, and probabilistic maximum variance) which leverage our scalable GP framework in sparse reward environments. Finally, we show favorable comparison against the bootstrapped deep Q-Network.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1986

Preconditioning matrices for Chebyshev derivative operators

The problem of preconditioning the matrices arising from pseudo-spectral Chebyshev approximations of first order operators is considered in both one and two dimensions. In one dimension a preconditioner represented by a full matrix which leads to preconditioned eigenvalues that are real, positive, and lie between 1 and pi/2, is already available. Since there are cases in which it is not computationally convenient to work with such a preconditioner, a large number of preconditioners were studied which were more sparse (in particular three and four diagonal matrices). The eigenvalues of such preconditioned matrices are compared. The results were applied to the problem of finding the steady state solution to an equation of the type u sub t = u sub x + f, where the Chebyshev collocation is used for the spatial variable and time discretization is performed by the Richardson method. In two dimensions different preconditioners are proposed for the matrix which arises from the pseudo-spectral discretization of the steady state problem. Results are given for the CPU time and the number of iterations using a Richardson iteration method for the unpreconditioned and preconditioned cases.

Rothman, Ernest E.↗
NASA NTRS1997

A Portable MPI Implementation of the SPAI Preconditioner in ISIS++

A parallel MPI implementation of the Sparse Approximate Inverse (SPAI) preconditioner is described. SPAI has proven to be a highly effective preconditioner, and is inherently parallel because it computes columns (or rows) of the preconditioning matrix independently. However, there are several problems that must be addressed for an efficient MPI implementation: load balance, latency hiding, and the need for one-sided communication. The effectiveness, efficiency, and scaling behavior of our implementation will be shown for different platforms.

Barnard, Stephen T.↗
NASA NTRS2007

Representation-Independent Iteration of Sparse Data Arrays

An approach is defined that describes a method of iterating over massively large arrays containing sparse data using an approach that is implementation independent of how the contents of the sparse arrays are laid out in memory. What is unique and important here is the decoupling of the iteration over the sparse set of array elements from how they are internally represented in memory. This enables this approach to be backward compatible with existing schemes for representing sparse arrays as well as new approaches. What is novel here is a new approach for efficiently iterating over sparse arrays that is independent of the underlying memory layout representation of the array. A functional interface is defined for implementing sparse arrays in any modern programming language with a particular focus for the Chapel programming language. Examples are provided that show the translation of a loop that computes a matrix vector product into this representation for both the distributed and not-distributed cases. This work is directly applicable to NASA and its High Productivity Computing Systems (HPCS) program that JPL and our current program are engaged in. The goal of this program is to create powerful, scalable, and economically viable high-powered computer systems suitable for use in national security and industry by 2010. This is important to NASA for its computationally intensive requirements for analyzing and understanding the volumes of science data from our returned missions.

James, Mark↗
NASA NTRS1994

Dermal Aged and Fetal Fibroblasts Realign in Response to Mechanical Strain

Integrins specifically recognize and bind extracellular matrix components, providing physical anchor points and functional setpoints. Focal adhesion complexes, containing integrin and cytoskeletal proteins, are potential mechanoreceptors, poised to distribute applied forces through the cytoskeleton. Pursuing the hypothesis that cells both perceive and respond to external force, we applied a stretch/relaxation regimen to normal human fetal and aged dermal fibroblast monolayers cultured on flexible membranes. The frequency and magnitude of the applied force is precisely controlled by the Flexercell Unit(Trademark). A protocol of stretch (20% elongation of the monolayer) at a frequency of 6 cycles/min caused a progressive change from a randomly distributed pattern of cells to a symmetric, radial distribution with cells aligned parallel to the applied force. We have coined the term 'orienteering' as the process of active alignment of cells in response to applied force. Cytochalasin D was added in graded doses to investigate the role of the actin cytoskeleton in force perception and transmission. A clear dose response was found; at high concentrations orienteering was abolished; and the drug's impact was reversible. The two cell strains used were similar in their alignment behavior and in their responses to cytochalasin D. Orienteering was influenced by cell density, and the cell strains studied differed in this respect. Fetal cells, unlike their aged counterparts, failed to orient at high cell density. In both cell strains, mid-density cultures aligned rapidly and sparse cultures lagged. These results indicate that both cell-cell adhesion and cytoskeleton integrity are critical in mediating the orienteering response. Differences between these two cell strains may relate to their expression of extracellular matrix molecules (fibronectin, collagen type 1) integrins and their relative binding affinities.

Sawyer, Christine↗
DOE OSTI2023

Deployment of ADTimePix3 areaDetector Driver at Neutron and X-ray User Facilities

TimePix3 is a 65k hybrid pixel readout chip with simultaneous Time-of-Arrival (ToA) and Time-over-Threshold (ToT) recording in each pixel*. The chip operates without a trigger signal with a sparse readout where only pixels containing events are read out. The flexible architecture allows 40 MHits/s/cm² readout throughput, using simultaneous readout and acquisition by sharing readout logic with transport logic of superpixel matrix formed using 2x4 structure. The chip ToA records 1.5625 ns time resolution. The X-ray and charged particle events are counted directly. However, indirect neutron counts use 6Li fission in a scintillator matrix, such as ZnS(Ag). The fission space-charge region is limited to 5-9 um. A photon from scintillator material excites a photocathode electron, which is further multiplied in dual-stack MCP. The neutron count event is a cluster of electron events at the chip. We report on the EPICS areaDetector** ADTimePix3 driver that controls Serval*** using json commands. The driver directs data to storage and to a real-time processing pipeline and configures the chip. The time-stamped data are stored in raw .tpx3 file format and passed through a socket where the clustering software identifies individual neutron events. The conventional 2D images are available as images for each exposure frame, and a preview is useful for sample alignment. The areaDetector driver allows integration of time-enhanced capabilities of this detector into SNS beamlines controls and unprecedented time resolution.

Gofron, Kaz↗
DOE OSTI2021

Vinculin is required for neuronal mechanosensing but not for axon outgrowth

Integrin receptors are transmembrane proteins that bind to the extracellular matrix (ECM). In most animal cell types integrins cluster together with adaptor proteins at focal adhesions that sense and respond to external mechanical signals. In the central nervous system (CNS), ECM proteins are sparsely distributed, the tissue is comparatively soft and neurons do not form focal adhesions. Thus, how neurons sense tissue stiffness is currently poorly understood. Here, we found that integrins and the integrin-associated proteins talin and focal adhesion kinase (FAK) are required for the outgrowth of neuronal processes. Vinculin, however, whilst not required for neurite outgrowth was a key regulator of integrin-mediated mechanosensing of neurons. During growth, growth cones of axons of CNS derived cells exerted dynamic stresses of around 10–12 Pa on their environment, and axons grew significantly longer on soft (0.4 kPa) compared to stiff (8 kPa) substrates. Depletion of vinculin blocked this ability of growth cones to distinguish between soft and stiff substrates. These data suggest that vinculin in neurons acts as a key mechanosensor, involved in the regulation of growth cone motility.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1991

Lanczos eigensolution method for high-performance computers

The theory, computational analysis, and applications are presented of a Lanczos algorithm on high performance computers. The computationally intensive steps of the algorithm are identified as: the matrix factorization, the forward/backward equation solution, and the matrix vector multiples. These computational steps are optimized to exploit the vector and parallel capabilities of high performance computers. The savings in computational time from applying optimization techniques such as: variable band and sparse data storage and access, loop unrolling, use of local memory, and compiler directives are presented. Two large scale structural analysis applications are described: the buckling of a composite blade stiffened panel with a cutout, and the vibration analysis of a high speed civil transport. The sequential computational time for the panel problem executed on a CONVEX computer of 181.6 seconds was decreased to 14.1 seconds with the optimized vector algorithm. The best computational time of 23 seconds for the transport problem with 17,000 degs of freedom was on the the Cray-YMP using an average of 3.63 processors.

Bostic, Susan W.↗
NASA NTRS1992

Rational approximations from power series of vector-valued meromorphic functions

Let F(z) be a vector-valued function, F: C yields C(sup N), which is analytic at z = 0 and meromorphic in a neighborhood of z = 0, and let its Maclaurin series be given. In this work we developed vector-valued rational approximation procedures for F(z) by applying vector extrapolation methods to the sequence of partial sums of its Maclaurin series. We analyzed some of the algebraic and analytic properties of the rational approximations thus obtained, and showed that they were akin to Pade approximations. In particular, we proved a Koenig type theorem concerning their poles and a de Montessus type theorem concerning their uniform convergence. We showed how optical approximations to multiple poles and to Laurent expansions about these poles can be constructed. Extensions of the procedures above and the accompanying theoretical results to functions defined in arbitrary linear spaces was also considered. One of the most interesting and immediate applications of the results of this work is to the matrix eigenvalue problem. In a forthcoming paper we exploited the developments of the present work to devise bona fide generalizations of the classical power method that are especially suitable for very large and sparse matrices. These generalizations can be used to approximate simultaneously several of the largest distinct eigenvalues and corresponding eigenvectors and invariant subspaces of arbitrary matrices which may or may not be diagonalizable, and are very closely related with known Krylov subspace methods.

Sidi, Avram↗
NASA NTRS1990

Partitioning sparse matrices with eigenvectors of graphs

The problem of computing a small vertex separator in a graph arises in the context of computing a good ordering for the parallel factorization of sparse, symmetric matrices. An algebraic approach for computing vertex separators is considered in this paper. It is shown that lower bounds on separator sizes can be obtained in terms of the eigenvalues of the Laplacian matrix associated with a graph. The Laplacian eigenvectors of grid graphs can be computed from Kronecker products involving the eigenvectors of path graphs, and these eigenvectors can be used to compute good separators in grid graphs. A heuristic algorithm is designed to compute a vertex separator in a general graph by first computing an edge separator in the graph from an eigenvector of the Laplacian matrix, and then using a maximum matching in a subgraph to compute the vertex separator. Results on the quality of the separators computed by the spectral algorithm are presented, and these are compared with separators obtained from other algorithms for computing separators. Finally, the time required to compute the Laplacian eigenvector is reported, and the accuracy with which the eigenvector must be computed to obtain good separators is considered. The spectral algorithm has the advantage that it can be implemented on a medium-size multiprocessor in a straightforward manner.

Pothen, Alex↗