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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 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Machine-learning prediction for quasiparton distribution function matrix elements

There have been rapid developments in the direct calculation in lattice QCD (LQCD) of the Bjorken-x dependence of hadron structure through large-momentum effective theory (LaMET). LaMET overcomes the previous limitation of LQCD to moments (that is, integrals over Bjorken x) of hadron structure, allowing LQCD to directly provide the kinematic regions where the experimental values are least known. LaMET requires large-momentum hadron states to minimize its systematics and allow us to reach small- x reliably. This means that very fine lattice spacing to minimize lattice artifacts at order $(P_za)^n$ will become crucial for next-generation LaMET-like structure calculations. Furthermore, such calculations require operators with long Wilson-link displacements, especially in finer lattice units, increasing the communication costs relative to that of the propagator inversion. In this work, we explore whether machine-learning algorithms can make predictions of correlators to reduce the computational cost of these LQCD calculations. We consider two algorithms, gradient-boosting decision tree and linear models, applied to LaMET data, the matrix elements needed to determine the kaon and $η_s$ unpolarized parton distribution functions (PDFs), meson distribution amplitude (DA), and the nucleon gluon PDF. We find that both algorithms can reliably predict the target observables with different prediction accuracy and systematic errors. The predictions from smaller displacement z to larger ones work better than those for momentum p due to the higher correlation among the data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Three-dimensional imaging using coherent x rays at grazing incidence geometry

We have developed a three-dimensional coherent diffraction imaging algorithm to retrieve phases of diffraction patterns of samples in grazing incidence small angle x-ray scattering experiments. The algorithm interprets the diffraction patterns using the distorted-wave Born approximation instead of the Born approximation, as in this case, the existence of a reflected beam from the substrate causes the diffraction pattern to deviate significantly from the simple Fourier transform of the object. Detailed computer simulations show that the algorithm works. Verification with real experiments is planned.

Yang, Yi↗
DOE OSTI2023

Dynamic Network Analysis of Nuclear Science Literature for Research Influence Assessment

Analyzing nuclear science literature via data-driven methods is a critical step for assessing research influence and technology advancements. Indicators of scholarly activities may be buried in large volumes of nuclear research publications and collaboration networks over time. Mining for relevant scholarly influence trends in large volumes of text can be computationally challenging; however, open-source information on research collaborations over time can offer opportunities to extract meaningful insights. While network centrality analysis of scholarly research provides topology-based insights, additional emphasis on dynamics associated with the diffusion of information through these networks is important. Here this paper represents a step in that direction through the development of a novel dynamic network analysis framework and computational engine to identify key entities and capabilities over time within global scholarly nuclear science collaboration networks. Network theoretic, stochastic simulation, and optimization methods are leveraged to address variability in scholarly interactions, influence propagation, and collaboration patterns via network connections. A topic-aware influence maximization algorithm is developed to address the goal of identifying key influential authors in diverse research topics over time. Efficient parallelized implementation of the algorithm is applied to reduce computational costs. A proof-of-concept case study using open-source Scopus data with 33,517 published nuclear research papers from 2000-2019 is presented and representative analytic insights are generated. Broad implications of these insights are discussed and future research directions are also identified.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2020

Density Matrix Quantum Circuit Simulation via the BSP Machine on Modern GPU Clusters

As quantum computers evolve, simulations of quantum programs on classical computers will be essential in validating quantum algorithms, understanding the effect of system noise, and design applications for future quantum computers. In this paper, we propose a novel multi-GPU programming model called MG-BSP that constructs a virtual BSP machine on top of modern multi-GPU platforms, and tweak the programming model to build a multi-GPU density matrix quantum simulator. We propose and evaluated a new formulation minimizing communication and prove that the transformation conserves original semantics when noise is introduced. We build the tool-chain to support quantum assembly open standard, synthesize testing quantum circuit, and enable ultra-deep quantum simulation. We evaluated our design on four state-of-the-art multi-GPU platforms including the latest DGX-1 and DGX-2 systems. We demonstrate simulation of 1 million gates in 94 minutes, far deeper circuits than has been demonstrated in prior work. A roofline-model analysis show that we have reached near-optimal performance under memory bound.

Li, Ang↗
DOE OSTI2022

Vector-Matrix Multiplication Engine for Neuromorphic Computation with a CBRAM Crossbar Array [Slides]

The core function of many neural network algorithms is the dot product, or vector matrix multiply (VMM) operation. Crossbar arrays utilizing resistive memory elements can reduce computational energy in neural algorithms by up to five orders of magnitude compared to conventional CPUs. Moving data between a processor, SRAM, and DRAM dominates energy consumption. By utilizing analog operations to reduce data movement, resistive memory crossbars can enable processing of large amounts of data at lower energy than conventional memory architectures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Verifying the Computational Integrity of Power Grid Controls with Zero-Knowledge Proof

The control of future power grids is migrating from a centralized to a distributed/decentralized scheme to enable a massive penetration of distributed energy resources with rising dependence on communication infrastructure. A common assumption made for most existing distributed/decentralized controllers is that local controllers would faithfully follow the designated controller dynamics based on the data received from communication channels. However, with increased probability of cyberattacks on Operational Technology infrastructure, such an assumption could be risky because proper execution of the controller dynamics is then built on trust in secure communication and computation. In this work, we leverage a cryptography technology known as zero-knowledge scalable transparent arguments of knowledge (zk-STARK) to verify the computational integrity of power grid control algorithms, with projected linear dynamics-based control schemes as the initial proof-of-concept test case. The method presented here converts the cybersecurity challenge of data integrity for grid control into a subset of computational integrity.

computational integrity↗
DOE OSTI2025

Accelerating iterative ptychography with an integrated neural network

Electron ptychography is a powerful and versatile tool for high-resolution and dose-efficient imaging. Iterative reconstruction algorithms are powerful but also computationally expensive due to their relative complexity and the many hyperparameters that must be optimised. Gradient descent-based iterative ptychography is a popular method, but it may converge slowly when reconstructing low spatial frequencies. Here, in this work, we present a method for accelerating a gradient descent-based iterative reconstruction algorithm by training a neural network (NN) that is applied in the reconstruction loop. The NN works in Fourier space and selectively boosts low spatial frequencies, thus enabling faster convergence in a manner similar to accelerated gradient descent algorithms. We discuss the difficulties that arise when incorporating a NN into an iterative reconstruction algorithm and show how they can be overcome with iterative training. We apply our method to simulated and experimental data of gold nanoparticles on amorphous carbon and show that we can significantly speed up ptychographic reconstruction of the nanoparticles.

4DSTEM↗
DOE OSTI2023

Binary Control Pulse Optimization for Quantum Systems

Quantum control aims to manipulate quantum systems toward specific quantum states or desired operations. Designing highly accurate and effective control steps is vitally important to various quantum applications, including energy minimization and circuit compilation. In this paper we focus on discrete binary quantum control problems and apply different optimization algorithms and techniques to improve computational efficiency and solution quality. Specifically, we develop a generic model and extend it in several ways. We introduce a squared L 2 -penalty function to handle additional side constraints, to model requirements such as allowing at most one control to be active. We introduce a total variation (TV) regularizer to reduce the number of switches in the control. We modify the popular gradient ascent pulse engineering (GRAPE) algorithm, develop a new alternating direction method of multipliers (ADMM) algorithm to solve the continuous relaxation of the penalized model, and then apply rounding techniques to obtain binary control solutions. We propose a modified trust-region method to further improve the solutions. Our algorithms can obtain high-quality control results, as demonstrated by numerical studies on diverse quantum control examples.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Particle-Tracking Proton Computed Tomography—Data Acquisition, Preprocessing, and Preconditioning

Proton CT (pCT) is a promising new imaging technique that can reconstruct relative stopping power (RSP) more accurately than x-ray CT in each cubic millimeter voxel of the patient. This, in turn, will result in better proton range accuracy and, therefore, smaller planned tumor volumes (PTV). The hardware description and some reconstructed images have previously been reported. In a series of two contributions, we focus on presenting the software algorithms that convert pCT detector data to the final reconstructed pCT images for application in proton treatment planning. There were several options on how to accomplish this, and we will describe our solutions at each stage of the data processing chain. In the first paper of this series, we present the data acquisition with the pCT tracking and energy-range detectors and how the data are preprocessed, including the conversion to the well-formatted track information from tracking data and water-equivalent path length from the data of a calibrated multi-stage energy-range detector. These preprocessed data are then used for the initial image formation with an FDK cone-beam CT algorithm. The output of data acquisition, preprocessing, and FDK reconstruction is presented along with illustrative imaging results for two phantoms, including a pediatric head phantom. The second paper in this series will demonstrate the use of iterative solvers in conjunction with the superiorization methodology to further improve the images resulting from the upfront FDK image reconstruction and the implementation of these algorithms on a hybrid CPU/GPU computer cluster.

42 ENGINEERING↗
DOE OSTI2022

Identifying Disinformation Using Rhetorical Devices in Natural Language Models

Foreign disinformation campaigns are strategically organized, extended efforts using disinformation – false or misleading information deliberately placed by an adversary – to achieve some goal. Disinformation campaigns pose severe threats to our nation’s security by misinforming decision makers and negatively influencing their actions when they are operating on limited amounts of evidence. Current efforts rely on subject matter experts to manually identify disinformation, or on computers and traditional natural language processing algorithms to identify patterns in data to calculate the probability that something is disinformation or not. While both have their merits and successes, subject matter experts are unable to keep up with the high volumes of global information and traditional natural language algorithms do not do well in identifying “why” something is disinformation or not. Our hypothesis is that we can identify disinformation by looking at the way someone speaks, in the rhetorical devices they use. We have curated and annotated a dataset designed for multiple natural language processing tasks, but specifically useful for disinformation detection algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Multiparticle cumulant mapping for Coulomb explosion imaging: Calculations and algorithm

We present a versatile cumulant mapping algorithm for analyzing correlated particle emission, offering insights into complex electronic and nuclear dynamics. Recently, we have demonstrated the use of cumulant mapping to extract information-rich correlations between the momenta of multiple fragments produced in Coulomb explosion imaging experiments [C. Cheng et al., Phys. Rev. Lett. 130, 093001 (2023)]. We define cumulant mapping in terms of histograms, enabling fast computation of linear (additive) observables. However, applying the same algorithm to nonlinear (nonadditive) observables poses challenges, as the computation time of conventional estimators scales nonlinearly with data size. To overcome this, we develop estimators and an accompanying algorithm to enable computationally efficient estimation of the cumulant of interest. Comparisons of computation times and signal-to-noise ratios reveal the superior performance of our approach. This method is demonstrated on the (D+, D+, C+, O+) dissociation channel of CD 2 ⁢O 4+ produced in a strong-field ionization experiment. Additionally, Poisson statistics are used to simulate the two methods and provide insights into the efficiency of our algorithm. The proposed methodology unlocks efficient computation of cumulant mapping for a broader range of complex systems and observables, such as the laser pulse dependence of ionization dynamics.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2023

Characterizing and mitigating coherent errors in a trapped ion quantum processor using hidden inverses

Quantum computing testbeds exhibit high-fidelity quantum control over small collections of qubits, enabling performance of precise, repeatable operations followed by measurements. Currently, these noisy intermediate-scale devices can support a sufficient number of sequential operations prior to decoherence such that near term algorithms can be performed with proximate accuracy (like chemical accuracy for quantum chemistry problems). While the results of these algorithms are imperfect, these imperfections can help bootstrap quantum computer testbed development. Demonstrations of these algorithms over the past few years, coupled with the idea that imperfect algorithm performance can be caused by several dominant noise sources in the quantum processor, which can be measured and calibrated during algorithm execution or in post-processing, has led to the use of noise mitigation to improve typical computational results. Conversely, benchmark algorithms coupled with noise mitigation can help diagnose the nature of the noise, whether systematic or purely random. Here, we outline the use of coherent noise mitigation techniques as a characterization tool in trapped-ion testbeds. We perform model-fitting of the noisy data to determine the noise source based on realistic physics focused noise models and demonstrate that systematic noise amplification coupled with error mitigation schemes provides useful data for noise model deduction. Further, in order to connect lower level noise model details with application specific performance of near term algorithms, we experimentally construct the loss landscape of a variational algorithm under various injected noise sources coupled with error mitigation techniques. This type of connection enables application-aware hardware codesign, in which the most important noise sources in specific applications, like quantum chemistry, become foci of improvement in subsequent hardware generations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

ED-cPSD: Fast Phase-Size Distribution via Sequential Erosion-Dilation

The Erosion-Dilation continuous Phase-Size Distribution, ED-cPSD, is an application for calculating continuous pore and particle-size distribution from digital reconstructions and/or image-based structural data. It is based on the erosion-dilation continuous phase-size distribution method. A continuous size distribution is a measure of the probability density of finding a particle or pore of a certain size. These distributions are of interest in any field of study involving porous media, including but not limited to electrochemistry, petroleum engineering, geology, and food science. The algorithm behind the software provides a computationally efficient way to calculate phase-size distributions for large domains. For a 3D battery electrode reconstruction with 1.3 x 10 8 voxels, the particle size distribution is derived in under 2 min on a desktop, while also retaining flexibility and computational efficiency for HPC-scale multi-threading. The software can handle structures with over 10 9 voxels. The algorithm is roughly 280 times faster than a previous version on the same task.

Characterization↗
DOE OSTI2023

High-Level Synthesis of Irregular Applications: A Case Study on Influence Maximization

The Influence Maximization problem is the problem of identifying a small cohort of actors from a broader population that, when initially activated in a diffusion process, are expected to result in a large number of activations in the population. While the problem is known to be NP-hard, several approximation algorithms have been devised by leveraging its submodular structure. While these algorithms are theoretically efficient, they are computationally very expensive in practice. This work advances the current state-of-the-art parallelization scheme for the IMM algorithm by devising the adoption of custom hardware accelerators implemented on FPGAs by leveraging High Level Synthesis from OpenCL. We study the performance of our proposed approach by exploring optimizations tailored at improving the parallel efficiency of the accelerators and highlight their effects and limitations in accelerating complex graph analytic applications. Our experimental evaluation shows that FPGA acceleration can improve the performance of the LT diffusion model up to 1.72x for the entire application and up to 2.90x for its most important kernel with respect to a CPU only parallel execution. The FPGA acceleration of the LT model shows also a 1.54x reduction in energy consumption when compared to a parallel CPU only run.

Neff, Reece W.↗
DOE OSTI2020

Noisy Intermediate-Scale Quantum Applications on a Pathfinder System

Work performed under this one-year LDRD was concerned with estimating resource requirements for small quantum test beds that are expected to be available in the near future. This work represents a preliminary demonstration of our ability to leverage quantum hardware for solving small quantum simulation problems in areas of interest to the DOE. The algorithms enabling such studies are hybrid quantum-classical variational algorithms, in particular the widely-used variational quantum eigensolver (VQE). Employing this hybrid algorithm, in which the quantum computer complements the classical one, we implemented an end-to-end application-level toolchain that allows the user to specify a molecule of interest and compute the ground state energy using the VQE approach. We found significant limitations attributable to the classical portion of the hybrid system, including a greater than greater-than-quartic power scaling of the classical memory requirements with the system size. Current VQE approaches would require an exascale machine for solving any molecule with size greater than 150 nuclei. Our findings include several improvements that we implemented into the VQE toolchain, including a new classical optimizer that is decades old but hadn't been considered before in the VQE ecosystem. Our findings suggest limitations to variational hybrid approaches to simulation that further motivate the need for a gate-based fault-tolerant quantum processor that can implement larger problems using the fully digital quantum phase estimation algorithm.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Computing Free Energies with Fluctuation Relations on Quantum Computers

As a central thermodynamic property, free energy enables the calculation of virtually any equilibrium property of a physical system, allowing for the construction of phase diagrams and predictions about transport, chemical reactions, and biological processes. Thus, methods for efficiently computing free energies, which in general is a difficult problem, are of great interest to broad areas of physics and the natural sciences. The majority of techniques for computing free energies target classical systems, leaving the computation of free energies in quantum systems less explored. Recently developed fluctuation relations enable the computation of free energy differences in quantum systems from an ensemble of dynamic simulations. While performing such simulations is exponentially hard on classical computers, quantum computers can efficiently simulate the dynamics of quantum systems. Here, we present an algorithm utilizing a fluctuation relation known as the Jarzynski equality to approximate free energy differences of quantum systems on a quantum computer. In this work we discuss under which conditions our approximation becomes exact, and under which conditions it serves as a strict upper bound. Furthermore, we successfully demonstrate a proof of concept of our algorithm using the transverse field Ising model on a real quantum processor. As quantum hardware continues to improve, we anticipate that our algorithm will enable computation of free energy differences for a wide range of quantum systems useful across the natural sciences.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Constructing field-aligned coordinate systems for gyrokinetic simulations of tokamaks in X-point geometries

Structures in tokamak plasmas are elongated along the direction of the magnetic field and short in the directions perpendicular to the magnetic field. Many tokamak simulation codes take advantage of this by using a field-aligned coordinate system. However, field-aligned coordinate systems have a coordinate singularity at magnetic X-points where the poloidal magnetic field vanishes, which makes it difficult to use field-aligned coordinate systems when simulating the core and scrape-off layer simultaneously. Here, we present an algorithm for grid generation and computing geometric quantities in a standard field-aligned coordinate system that avoids the singularity and allows one to conduct two-dimensional gyrokinetic axisymmetric simulations in X-point geometries. Convergence tests of advection, boundary value problems and geometric quantities all show greater than first-order convergence even in the vicinity of the X-point. We also demonstrate the geometric consistency of our algorithm with an example simulation of the spherical tokamak for energy production, which shows machine-precision particle conservation.

fusion plasma↗