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At least 451 records · Page 25

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NASA NTRS1991

Monte Carlo simulation of errors in the anisotropy of magnetic susceptibility - A second-rank symmetric tensor

Monte Carlo perturbations of synthetic tensors to evaluate the Hext/Jelinek elliptical confidence regions for anisotropy of magnetic susceptibility (AMS) eigenvectors are used. When the perturbations are 33 percent of the minimum anisotropy, both the shapes and probability densities of the resulting eigenvector distributions agree with the elliptical distributions predicted by the Hext/Jelinek equations. When the perturbation size is increased to 100 percent of the minimum eigenvalue difference, the major axis of the 95 percent confidence ellipse underestimates the observed eigenvector dispersion by about 10 deg. The observed distributions of the principal susceptibilities (eigenvalues) are close to being normal, with standard errors that agree well with the calculated Hext/Jelinek errors. The Hext/Jelinek ellipses are also able to describe the AMS dispersions due to instrumental noise and provide reasonable limits for the AMS dispersions observed in two Hawaiian basaltic dikes. It is concluded that the Hext/Jelinek method provides a satisfactory description of the errors in AMS data and should be a standard part of any AMS data analysis.

Lienert, Barry R.↗

NASA NTRS1992

A quadratic-tensor model algorithm for nonlinear least-squares problems with linear constraints

A new algorithm for solving nonlinear least-squares and nonlinear equation problems is proposed which is based on approximating the nonlinear functions using the quadratic-tensor model by Schnabel and Frank. The algorithm uses a trust region defined by a box containing the current values of the unknowns. The algorithm is found to be effective for problems with linear constraints and dense Jacobian matrices.

Hanson, R. J.↗

NASA NTRS1994

Coronal heating by the resonant absorption of Alfven waves: The effect of viscous stress tensor

The time-dependent linearized magnetohydrodynamics (MHD) equations for a fully compressible, low-beta, viscoresistive plasma are solved numerically using an implicit integration scheme. The full viscosity stress tensor (Braginskii 1965) is included with the five parameters eta(sub i) i = 0 to 4. In agreement with previous studies, the numerical simulations demonstrate that the dissipation on inhomogeneities in the background Alfven speed occurs in a narrow resonant layer. For an active region in the solar corona the values of eta(sub i) are eta(sub o) = 0.65 g/cm/s, eta(sub 1) = 3.7 x 10(exp -12) g/cm/s, eta(sub 2) = 4 eta(sub 1), eta(sub 3) = 1.4 x 10(exp -6) g/cm/s, eta(sub 4) = 2 eta(sub 3), with n = 10(exp 10)/cu cm, T = 2 x 10(exp 6) K, and B = 100 G. When the Lundquist number S = 10(exp 4) and R(sub 1) much greater than S (where R(sub 1) is the dimensionless shear viscous number) the width of the resistive dissipation layer d(sub r) is 0.22a (where a is the density gradient length scale) and d(sub r) approximately S(exp -1/3). When S much greater than R(sub 1) the shear viscous dissipation layer width d(sub r) scales as R(sub 1)(exp -1/3). The shear viscous and the resistive dissipation occurs in an overlapping narrow region, and the total heating rate is independent of the value of the dissipation parameters in agreement with previous studies. Consequently, the maximum values of the perpendicular velocity and perpendicular magnetic field scale as R(sub 1)(exp -1/3). It is evident from the simulations that for solar parameters the heating due to the compressive viscosity (R(sub 0) = 560) is negligible compared to the resistive and the shear viscous (R(sub 1)) dissipation and it occurs in a broad layer of order a in width. In the solar corona with S approximately equals 10(exp 4) and R(sub 1) approximately equals 10(exp 14) (as calculated from the Braginskii expressions), the shear viscous resonant heating is of comparable magnitude to the resistive resonant heating.

Ofman, L.↗

NASA NTRS1995

Tensoral Present and Future

The coding of high-performance fluids simulations requires significant knowledge of both numerical and computational details. The magnitude and complexity of low-level details is often enough to discourage many users of turbulence data wishing to study more important, higher-level fluid dynamical questions. These same complexities are often a practical barrier to simulation experts who develop, verify and maintain the codes which generate this data. Future fluids codes, with high resolution and complex geometries, are likely to involve far more coding complexity. The research--the design and implementation of the Tensoral computer language--aims to greatly ease the coding of today's simulation and post-processing codes and at the same time provide a general computational tool for future simulations.

Dresselhaus, Eliot↗

NASA NTRS2003

Tensor Fields for Use in Fractional-Order Viscoelasticity

To be able to construct viscoelastic material models from fractional0order differentegral equations that are applicable for 3D finite-strain analysis requires definitions for fractional derivatives and integrals for symmetric tensor fields, like stress and strain. We define these fields in the body manifold. We then map them ito spatial fields expressed in terms of an Eulerian or Lagrangian reference frame where most analysts prefer to solve boundary problems.

Freed, Alan D.↗

NASA NTRS2006

In flight estimations of Cassini spacecraft inertia tensor and thruster magnitude

This paper describes two methods used by the Cassini Attitude Control team to determine these key parameters and how flight telemetry was used to estimate them. The method for estimating the spacecraft inertia tensor exploits the conservation of angular momentum during spacecraft slews under reaction wheel control.

inertia properties↗

NASA NTRS2007

HST Inertia Tensor Optimization for Two-Gyro Science

The power point presentation includes an overview of the Hubble Space Telescope, pointing control hardware peculiarities, the two-gyro science (TGS) control system, TGS modifications to preserved hardware lifetime, chasing-down disturbance torques less than or equal to 0.002 Nm, inertia tensor optimization, and a summary with lessons learned.

Clapp, Brian↗

NASA NTRSDate not supplied

Exploring Quantum State Preparation Using Tensor Networks and Sparse Wavefunction Simulations

The variational quantum eigenvalue solver is a powerful hybrid quantum-classical approach that has been suggested as a candidate method to run on near-term quantum hardware for computing ground state electronic energies of molecular systems. However, even for small molecules, the number of variational parameters and qubits required to minimize the electronic energy is beyond the reach of current quantum computers except for small basis sets. We explore a new paradigm for state preparation where we test how much of the optimization can be approximately prepared with classical computers to reduce the number of optimization steps performed using a quantum device. By adapting a recent algorithm for the factorized form of the UCC ansatz, we can study molecular electronic structure problems with up to 64 qubits. In addition, we also test a related approach of using tensor networks to optimize quantum circuits in order to benchmark various lattice models. We present results using these approaches and discuss strategies for incorporating these ideas into variational algorithms involving near-term quantum computers. Our results help demonstrate the strength of the UCC ansatz and address pressing questions about optimal initial parameterizations and circuit construction.

quantum computing↗

DOE OSTI2021

Athena: High-Performance Sparse Tensor Contraction Sequence on Heterogeneous Memory

Sparse tensor contraction sequence has been widely employed in many fields, such as chemistry and physics. However, how to efficiently implement the sequence faces multiple challenges, such as redundant computations and memory operations, massive memory consumption, and inefficient utilization of hardware. To address the above challenges, we introduce Athena, a high-performance framework for SpTC sequences. Athena introduces new data structures, leverages emerging Optane-based heterogeneous memory (HM) architecture, and stage parallelism. In particular, Athena introduces shared hash table-represented sparse accumulator to eliminate unnecessary input processing and data migration; Athena uses a novel data-semantic guided dynamic migration solution to make the best use of the Optane-based HM for high performance; Athena also co-runs execution phases with different characteristics to enable high hardware utilization. Evaluating with 12 datasets, we show that Athena brings 327-7362× speedup over the state-of-the-art SpTC algorithm. With the dynamic data placement guided by data semantics, Athena brings performance improvement on Optane-based HM over a state-of-the-art software-based data management solution, a hardware-based data management solution, and PMM-only by 1.58×, 1.82×, and 2.34× respectively.

Liu, Jiawen↗

DOE OSTI2021

Union: A Unified HW-SW Co-Design Ecosystem in MLIR for Evaluating Tensor Operationson Spatial Accelerators

To meet the extreme compute demands for deep learning across commercial and scientific applications, dataflow accelerators are becoming increasingly popular. While these“domain-specific” accelerators are not fully programmable like CPUs and GPUs, they retain varying levels of flexibility with respect to data orchestration, i.e., dataflow and tiling optimizations to enhance efficiency. There are several challenges when designing new algorithms and mapping approaches to execute the algorithms for a target problem on new hardware. Previous works have addressed these challenges individually. To address this challenge as a whole, in this work, we present an HW-SW co-design ecosystem for spatial accelerators called Union within the popular MLIR compiler infrastructure. Our framework allows exploring different algorithms and their mappings on several accelerator cost models. Union also includes a plug-and-play library of accelerator cost models and mappers which can easily be extended. The algorithms and accelerator cost models are connected via a novel mapping abstraction that captures the map space of spatial accelerators which can be systematically pruned based on constraints from the hardware, workload, and mapper. We demonstrate the value of Union for the community with several case studies which examine offloading different tensor operations (CONV/GEMM/Tensor Contraction) on diverse accelerator architectures using different mapping schemes.

Jeong, Geonhwa↗

DOE OSTI2026

Tensor renormalization group approach to the $O(2)$ models via symmetry-twisted partition functions

We investigate critical phenomena in the $O(2)$ models using symmetry-twisted partition functions that can be efficiently computed within the tensor renormalization group framework. We first demonstrate, taking the three-dimensional model as an example, that symmetry-twisted partition functions detect the spontaneous breaking of global continuous symmetry. We then consider the same model in two dimensions, where the Berezinskii--Kosterlitz--Thouless (BKT) transition occurs. Since symmetry-twisted partition functions directly provide the helicity modulus at a finite twist angle, we determine the BKT transition point. These results are presented based on Ref.~\cite{Akiyama:2026dzg}. Finally, in addition to the original paper~\cite{Akiyama:2026dzg}, we apply this approach to the two-dimensional generalized $O(2)$ model and confirm that it successfully identifies the phase transitions between the ferromagnetic and nematic phases, as well as between the nematic and paramagnetic phases.

Akiyama, Shinichiro [Tsukuba U., CCS; Tokyo U., IC↗

DOE OSTI2024

Bayesian Tensor Decompositions for Scalable Supervised Learning of Scientific Data (Final Report)

In this document we highlight the detailed accomplishments and progress that we have made in this period. This progress seeks to address the three main objectives to provide new algorithms for quantifying uncertainty in low-multilinear-rank models and to leverage them for data analysis. These include: (1) develop probabilistic models for low-multilinear-rank functions; (2) develop a suite of Bayesian learning approaches to learn the probabilistic models from data; (3) apply the techniques on challenging problems arising in DOE-relevant applications.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024