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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

DELTACON: A Principled Massive-Graph Similarity Function with Attribution

How much did a network change since yesterday? How different is the wiring between Bob's brain (a left-handed male) and Alice's brain (a right-handed female)? Graph similarity with known node correspondence, i.e. the detection of changes in the connectivity of graphs, arises in numerous settings. In this work, we formally state the axioms and desired properties of the graph similarity functions, and evaluate when state-of-the-art methods fail to detect crucial connectivity changes in graphs. We propose DeltaCon, a principled, intuitive, and scalable algorithm that assesses the similarity between two graphs on the same nodes (e.g. employees of a company, customers of a mobile carrier). In our experiments on various synthetic and real graphs we showcase the advantages of our method over existing similarity measures. We also employ DeltaCon to real applications: (a) we classify people to groups of high and low creativity based on their brain connectivity graphs, and (b) do temporal anomaly detection in the who-emails-whom Enron graph.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

SU-C-304-02: Robust and Efficient Process for Acceptance Testing of Varian TrueBeam Linacs Using An Electronic Portal Imaging Device (EPID)

Purpose: The purpose of this project was to develop a process that utilizes the onboard kV and MV electronic portal imaging devices (EPIDs) to perform rapid acceptance testing (AT) of linacs in order to improve efficiency and standardize AT equipment and processes. Methods: In this study a Varian TrueBeam linac equipped with an amorphous silicon based EPID (aSi1000) was used. The conventional set of AT tests and tolerances was used as a baseline guide, and a novel methodology was developed to perform as many tests as possible using EPID exclusively. The developer mode on Varian TrueBeam linac was used to automate the process. In the current AT process there are about 45 tests that call for customer demos. Many of the geometric tests such as jaw alignment and MLC positioning are performed with highly manual methods, such as using graph paper. The goal of the new methodology was to achieve quantitative testing while reducing variability in data acquisition, analysis and interpretation of the results. The developed process was validated on two machines at two different institutions. Results: At least 25 of the 45 (56%) tests which required customer demo can be streamlined and performed using EPIDs. More than half of the AT tests can be fully automated using the developer mode, while others still require some user interaction. Overall, the preliminary data shows that EPID-based linac AT can be performed in less than a day, compared to 2–3 days using conventional methods. Conclusions: Our preliminary results show that performance of onboard imagers is quite suitable for both geometric and dosimetric testing of TrueBeam systems. A standardized AT process can tremendously improve efficiency, and minimize the variability related to third party quality assurance (QA) equipment and the available onsite expertise. Research funding provided by Varian Medical Systems. Dr. Sasa Mutic receives compensation for providing patient safety training services from Varian Medical Systems, the sponsor of this study.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2014

An automated target species selection method for dynamic adaptive chemistry simulations

The relative importance index (RII) approach for determining appropriate target species for dynamic adaptive chemistry (DAC) simulations using the directed relation graph with error propagation (DRGEP) method is developed. The adequacy and effectiveness of this RII method is validated for two fuels: n-heptane and isopentanol, representatives of a ground transportation fuel component and bio-alcohol, respectively. The conventional method of DRGEP target species selection involves picking an unchanging (static) set of target species based on the combustion processes of interest; however, these static target species may not remain important throughout the entire combustion simulation, adversely affecting the accuracy of the method. In particular, this behavior may significantly reduce the accuracy of the DRGEP-based DAC approach in complex multidimensional simulations where the encountered combustion conditions cannot be known a priori with high certainty. Moreover, testing multiple sets of static target species to ensure the accuracy of the method is generally computationally prohibitive. Instead, the RII method determines appropriate DRGEP target species solely from the local thermo-chemical state of the simulation, ensuring that accuracy will be maintained. Further, the RII method reduces the expertise required of users to select DRGEP target species sets appropriate to the combustion phenomena under consideration. Constant volume autoignition simulations run over a wide range of initial conditions using detailed reaction mechanisms for n-heptane and isopentanol show that the RII method is able to maintain accuracy even when traditional static target species sets fail, and are even more accurate than expert-selected target species sets. Additionally, the accuracy and efficiency of the RII method are compared to those of static target species sets in single-cell engine simulations under homogeneous charge compression ignition conditions. For simulations using more stringent DRGEP thresholds, the RII method performs similarly to that of the static target species sets. With a larger DRGEP threshold, the RII method is significantly more accurate than the static target species sets without imposing significant computational overhead. Moreover, the applicability of the RII method to a DRG-based DAC scheme is discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Information-incorporated gene network construction with FDR control

Abstract Motivation Large-scale gene expression studies allow gene network construction to uncover associations among genes. To study direct associations among genes, partial correlation-based networks are preferred over marginal correlations. However, FDR control for partial correlation-based network construction is not well-studied. In addition, currently available partial correlation-based methods cannot take existing biological knowledge to help network construction while controlling FDR. Results In this paper, we propose a method called Partial Correlation Graph with Information Incorporation (PCGII). PCGII estimates partial correlations between each pair of genes by regularized node-wise regression that can incorporate prior knowledge while controlling the effects of all other genes. It handles high-dimensional data where the number of genes can be much larger than the sample size and controls FDR at the same time. We compare PCGII with several existing approaches through extensive simulation studies and demonstrate that PCGII has better FDR control and higher power. We apply PCGII to a plant gene expression dataset where it recovers confirmed regulatory relationships and a hub node, as well as several direct associations that shed light on potential functional relationships in the system. We also introduce a method to supplement observed data with a pseudogene to apply PCGII when no prior information is available, which also allows checking FDR control and power for real data analysis. Availability and implementation R package is freely available for download at https://cran.r-project.org/package=PCGII.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Sparse Convolutional Neural Networks for particle classification in ProtoDUNE-SP events

Deep Learning (DL) methods and Computer Vision are becoming important tools for event reconstruction in particle physics detectors. In this work, we report on the use of submanifold sparse convolutional neural networks (SparseNets) for the classification of track and shower hits from a DUNE prototype liquid-argon detector at CERN (ProtoDUNE-SP). By taking advantage of the three-dimensional nature of the problem we use a set of nine input features to classify sparse and locally dense hits associated to track or shower particles. The SparseNet has been trained on a test sample and shows promising results: efficiencies and purities greater than 90%. This has also been achieved with a considerable speedup and substantially less resource utilization with respect to other DL networks such as graph neural networks. This method offers great scalability advantages for future large neutrino detectors such as the planned DUNE experiment.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Reduced-Order Models of Static Power Grids based on Spectral Clustering

For large-scale interconnected power systems that cover large geographical areas, certain electrical studies are required so that appropriate decisions ensure system reliability and low cost. For such studies, it is often neither practical nor necessary to model in detail the entire power system, which is increasingly complex due to a more diverse range of grid assets to choose from in both short and long-term planning. The goal of this paper is to present a methodology to reduce the order of large-scale power networks based on spectral graph theory given that current methods for static network reduction are not scalable. A brief analysis of some spectral clustering properties to determine which graph Laplacian matrix should be used and why is included. The analysis shows that the utilization of the normalized graph Laplacian is more advantageous for clustering purposes. Techniques are proposed to approximate cost functions for the aggregated generators. This is done via linear regression. The reduced-order model obtained with the proposed methodology has an accuracy above 94% and solves the scalability issue commonly present in other reduction methods. If the utilization of the reduced-order model is either constrained to load levels above mid-peak demand, or cost functions of aggregated units are approximated via a piecewise quadratic approach, then the error distribution is in the order of 10^-3. .

Baquedano-Aguilar, Mario D.↗
DOE OSTI2017

An improved hyperbolic embedding algorithm

Because hyperbolic space has properties that make it amenable to graph representations, there is significant interest in scalable hyperbolic-space embedding methods. These embeddings enable constant-time approximation of shortest-path distances, and so are significantly more efficient than full shortest-path computations. In this article, we improve on existing landmark-based hyperbolic embedding algorithms for large-scale graphs. Whereas previous methods compute the embedding by using the derivative-free Nelder–Mead simplex optimization method, our approach uses the limited-memory BFGS (LBFGS) method, which is quasi-Newton optimization, with analytic gradients. Our method is not only significantly faster but also produces higher-quality embeddings. Moreover, we are able to include the hyperbolic curvature as a variable in the optimization. We compare our hyperbolic embedding method implementation in Python (called Hypy) against the best publicly available software, Rigel. Our method is an order of magnitude faster and shows significant improvements in the accuracy of the shortest-path distance calculations. Furthermore, tests are performed on a variety of real-world networks, and we show the scalability of our method by embedding a graph with 1.8 billion edges and 65 million nodes.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1989

Multiprocessor performance modeling with ADAS

A graph managing strategy referred to as the Algorithm to Architecture Mapping Model (ATAMM) appears useful for the time-optimized execution of application algorithm graphs in embedded multiprocessors and for the performance prediction of graph designs. This paper reports the modeling of ATAMM in the Architecture Design and Assessment System (ADAS) to make an independent verification of ATAMM's performance prediction capability and to provide a user framework for the evaluation of arbitrary algorithm graphs. Following an overview of ATAMM and its major functional rules are descriptions of the ADAS model of ATAMM, methods to enter an arbitrary graph into the model, and techniques to analyze the simulation results. The performance of a 7-node graph example is evaluated using the ADAS model and verifies the ATAMM concept by substantiating previously published performance results.

Hayes, Paul J.↗
NASA NTRS2012

Interactive Web Graphs with Fewer Restrictions

There is growing popularity for interactive, statistical web graphs and programs to generate them. However, it seems that these programs tend to be somewhat restricted in which web browsers and statistical software are supported. For example, the software might use SVG (e.g., Protovis, gridSVG) or HTML canvas, both of which exclude most versions of Internet Explorer, or the software might be made specifically for R (gridSVG, CRanvas), thus excluding users of other stats software. There are more general tools (d3, Rapha lJS) which are compatible with most browsers, but using one of these to make statistical graphs requires more coding than is probably desired, and requires learning a new tool. This talk will present a method for making interactive web graphs, which, by design, attempts to support as many browsers and as many statistical programs as possible, while also aiming to be relatively easy to use and relatively easy to extend.

Fiedler, James↗
DOE OSTI2015

On the application of betweenness centrality in chemical network analysis: Computational diagnostics and model reduction

Here, the concept of shortest path and betweenness centrality (BC) is introduced in combustion systems, with its application demonstrated in chemical network analysis and skeletal mechanism generation. After establishing the graphical representation of a chemical network for given pressure, temperature, and species concentrations, a metric BC is defined to rank the shortest paths passed by other nodes between the pair of source and target node, and as such captures the dominant indirect kinetic pathways between any pair of nodes in the network for computational diagnostics. As a result, the controlling pathways are retained by collecting the species with larger BC values. Such a notion then indicates that the index of importance of species could then be assigned based on their BC values to further guide skeletal mechanism generation. Unlike existing methods, the betweenness centrality approach takes into account of both the fluxes between species and their relative positioning within the chemical network. To demonstrate its potential utility to combustion studies, the approach was applied to the GRI-3.0, LLNL and USC-Mech II mechanisms to identify the important pathways in the chemical network at each local reaction state, and develop skeletal mechanisms from all reaction state samples in auto-ignition and perfectly stirred reactor (PSR) simulations. The performance of the BC ranking is compared to the methods of directed relation graph (DRG), DRG with error propagations (DRGEP) and sensitivity analysis (SA), and is shown to possess sufficient utility in producing skeletal mechanisms with good accuracy and flexibility for the cases studied.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Active causal learning for decoding chemical complexities with targeted interventions

Abstract Predicting and enhancing inherent properties based on molecular structures is paramount to design tasks in medicine, materials science, and environmental management. Most of the current machine learning and deep learning approaches have become standard for predictions, but they face challenges when applied across different datasets due to reliance on correlations between molecular representation and target properties. These approaches typically depend on large datasets to capture the diversity within the chemical space, facilitating a more accurate approximation, interpolation, or extrapolation of the chemical behavior of molecules. In our research, we introduce an active learning approach that discerns underlying cause-effect relationships through strategic sampling with the use of a graph loss function. This method identifies the smallest subset of the dataset capable of encoding the most information representative of a much larger chemical space. The identified causal relations are then leveraged to conduct systematic interventions, optimizing the design task within a chemical space that the models have not encountered previously. While our implementation focused on the QM9 quantum-chemical dataset for a specific design task—finding molecules with a large dipole moment—our active causal learning approach, driven by intelligent sampling and interventions, holds potential for broader applications in molecular, materials design and discovery.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Artificial Intelligence Designer of Materials and Processes for Advanced Power Generation

In this presentation, ‘deep-freeze’ graphs, ‘convoluted filtering’ networks, ‘mirror-image’ graphs, and adversarial ensemble methods are utilized to support inversion modeling for optimization of the complex compositions and complex processes in design of high-performing alloys, with their properties tailored to the energy application specifications.

Romanov, Vyacheslav↗
DOE OSTI2021

Directional Laplacian Centrality for Cyber Situational Awareness

Cyber operations is drowning in diverse, high-volume, multi-source data. To get a full picture of current operations and identify malicious events and actors, analysts must see through data generated by a mix of human activity and benign automated processes. Although many monitoring and alert systems exist, they typically use signature-based detection methods. We introduce a general method rooted in spectral graph theory to discover patterns and anomalies without a priori knowledge of signatures. We derive and propose a new graph-theoretic centrality measure based on the derivative of the graph Laplacian matrix in the direction of a vertex. To build intuition about our measure, we show how it identifies the most central vertices in standard network datasets and compare to other graph centrality measures. Finally, we focus our attention on studying its effectiveness in identifying important IP addresses in network flow data. Using both real and synthetic network flow data, we conduct several experiments to test our measure’s sensitivity to two types of injected attack profiles and show that vertices participating in injected attack profiles exhibit noticeable changes in our centrality measures, even when the injected anomalies are relatively small, and in the presence of simulated network dynamics.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2021

Position Papers for the ASCR Workshop on Cybersecurity and Privacy for Scientific Computing Ecosystems

At the request of the Department of Energy's (DOE) Office of Advanced Scientific Computing Research (ASCR), this program committee has been tasked with organizing a workshop to identify basic research needs in cybersecurity and privacy to better support DOE's science and energy mission. As part of the process, the program committee is soliciting community input in the form of position papers to help identify significant use cases, facility issues, and other barriers to enabling verifiably trustworthy computational science while preserving data confidentiality as appropriate for scientific workflows of interest to DOE. The program committee will review these position papers and based on the fit of their area of expertise and interest, selected contributors will have the opportunity to participate in the workshop currently planned as a virtual event November 3-5th, 2021. The thrust areas that will be explored by this workshop are the following: (1) Algorithms for secure, scalable, privacy-enhancing technologies and frameworks, including: Federated AI/ML, Differential privacy, Randomized algorithms, Adversarial modeling & simulation, Graph algorithms, and Formal methods; (2) Platforms to support the entire scientific-computing ecosystem, including edge computing for large-scale experiments, focusing on heterogeneous systems and distributed systems, including: Heterogeneous computing systems, Distributed computing systems, and Secure data architectures; and (3) Data workflows to allow agile use of data while preserving integrity and privacy, making the important properties verifiable either at runtime or post-computation, including: Integrity and provenance and Data management infrastructure. Topics that are out-of-scope for the workshop include discussing specific proposed solutions or areas that are clearly out of DOE's fundamental and applied-sciences mission scope, e.g., cryptography, enterprise security, and general-operations technology.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Automated Detection of Instability-Inducing Channel Geometry Transitions in Saint-Venant Simulation of Large-Scale River Networks

A new sweep-search algorithm (SSA) is developed and tested to identify the channel geometry transitions responsible for numerical convergence failure in a Saint-Venant equation (SVE) simulation of a large-scale open-channel network. Numerical instabilities are known to occur at “sharp” transitions in discrete geometry, but the identification of problem locations has been a matter of modeler’s art and a roadblock to implementing large-scale SVE simulations. The new method implements techniques from graph theory applied to a steady-state 1D shallow-water equation solver to recursively examine the numerical stability of each flowpath through the channel network. The SSA is validated with a short river reach and tested by the simulation of ten complete river systems of the Texas–Gulf Coast region by using the extreme hydrological conditions recorded during hurricane Harvey. The SSA successfully identified the problematic channel sections in all tested river systems. Subsequent modification of the problem sections allowed stable solution by an unsteady SVE numerical solver. The new SSA approach permits automated and consistent identification of problem channel geometry in large open-channel network data sets, which is necessary to effectively apply the fully dynamic Saint-Venant equations to large-scale river networks or for city-wide stormwater networks.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1966

Flowgraph techniques for closed systems.

Flowgraph techniques for closed systems, discussing properties, approximation method, topology equation, frequency response, constraints, oscillatory and stochastic processes, etc

HARMONIC OSCILLATOR↗