Scalable Knowledge-Graph Analytics at 136 Petaflop/s
This dataset contains data that was presented and analyzed in our paper Scalable Knowledge-Graph Analytics at 136 Petaflop/s.
Herrmannova, Drahomira↗
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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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COVID-19 Knowledge Graph -- Dataset for SMCDC 2021 Challenge 2
This repository contains the data for the 2021 Smoky Mountains Computational Sciences Data Challenge (SMCDC21) Challenge 2 -- Finding Novel Links in COVID-19 Knowledge Graph. The total size of all files in this repository is 285MB. Challenge website: https://smc-datachallenge.ornl.gov/2021-challenge-2/ More information about the challenge and the data: https://github.com/ORNL/smcdc-2021-covid-kg
60 APPLIED LIFE SCIENCES↗
A software package for modeling and simulating fault graphs
This report describes a novel fault graph modeling language and a simulation tool for executing models specified in the language. The modeling language has three primary features that distinguish it from similar reliability analysis tools. These are (1) a random variable modeling several distinct outcomes of a single fault; (2) chains of faults in which one fault triggers another; and (3) time to fail sampled from probability distributions including positive normal, exponential, Weibull with a minimum, or immediate. These features are motivated by their use in a historical analysis of centrifuge reliability.
97 MATHEMATICS AND COMPUTING↗
Understanding Discrete Fracture Networks Through Spectral Graph Theory [Slides]
This presentation includes discussion of the use of spectral graph theory to understand fracture networks to reduce computational effort.
58 GEOSCIENCES↗
Graph-theoretical KMC Framework for Calculating Effective Diffusivity in TPBAR Components: Effective Diffusivity of Tritium in α-Zr Grain Boundaries
We report the development of a computer simulation tool based on the Graph-theoretical kinetic Monte Carlo (GT-KMC) approach that can be used to simulate diffusion and derive effective diffusivity of species in complex structures of TPBAR components. The GT-KMC framework was implemented in AKSOME, an on-lattice self-learning kinetic Monte Carlo tool developed at PNNL and was previously used to study solute diffusion in metal alloys. This newly developed tool will be helpful in simulating atom diffusion and extracting their effective diffusivities in grain boundaries and interfaces in various TPBAR components. Once benchmarked, the tool will be used to simulate the diffusion of tritium (hydrogen) along grain boundaries in α-Zr using the activation energy barrier data previously obtained from Density Functional Theory calculations.
36 MATERIALS SCIENCE↗
Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets
Deep learning models have been shown to outperform methods that rely on summary statistics, like the power spectrum, in extracting information from complex cosmological data sets. However, due to differences in the subgrid physics implementation and numerical approximations across different simulation suites, models trained on data from one cosmological simulation show a drop in performance when tested on another. Similarly, models trained on any of the simulations would also likely experience a drop in performance when applied to observational data. Training on data from two different suites of the CAMELS hydrodynamic cosmological simulations, we examine the generalization capabilities of Domain Adaptive Graph Neural Networks (DA-GNNs). By utilizing GNNs, we capitalize on their capacity to capture structured scale-free cosmological information from galaxy distributions. Moreover, by including unsupervised domain adaptation via Maximum Mean Discrepancy (MMD), we enable our models to extract domain-invariant features. We demonstrate that DA-GNN achieves higher accuracy and robustness on cross-dataset tasks. Using data visualizations, we show the effects of domain adaptation on proper latent space data alignment. This shows that DA-GNNs are a promising method for extracting domain-independent cosmological information, a vital step toward robust deep learning for real cosmic survey data.
79 ASTRONOMY AND ASTROPHYSICS↗
Detection of Overlapping Communities in Undirected Graphs with Personalized PageRank
In this document, we investigate a method to detect overlapping communities in undirected graphs. This project is ongoing work in collaboration1 with Trevor Steil at Lawrence Livermore National Laboratory (LLNL).
97 MATHEMATICS AND COMPUTING↗
Pedestrian-Involved Traffic Signal Optimization Using Decentralized Graph-based Multi-Agent Reinforcement Learning
Incorporating pedestrian movements in traffic signal timing optimization is essential for ensuring smooth and safe urban transportation systems. To develop an effective signal timing plan, it is crucial to comprehend how various pedestrian accommodation strategies impact both vehicle and pedestrian flow. This study explores the application of the Decentralized Graph-based Multi-Agent Reinforcement Learning (DGMARL) method for signal timing optimization. It considers both pedestrian and vehicle traffic state and assesses the effects of fixed and adaptive pedestrian request response on traffic and signal timing. The evaluation of DGMARL encompasses vehicles Eco_PI which is fuel consumption impact related to vehicle stops and stop delay, and pedestrian waiting time to ensure overall system efficiency. The proposed approach was evaluated using a Digital Twin microscopic traffic simulation model of MLK Smart Corridor in Chattanooga, Tennessee. The evaluation outcomes of vehicles Eco_PI, pedestrian waiting time and serving time are compared with the actuated and DGMARL signal timing with fixed pedestrian recall and minimum recall to determine the most suitable approaches for various traffic conditions. The results indicated that, on average, the strategy of Signal timing optimization with adaptive pedestrian request response improved the Eco_PI by 32.43%, and Signal timing optimization with both automated pedestrian traffic demand and adaptive pedestrian request response improved the Eco_PI by 31.62% compared to the actuated and DGMARL signal timing with fixed pedestrian recall and minimum recall.
K Kumarasamy, Vijayalakshmi↗
Expanding the representation of aerosol, cloud, and precipitation processes with graph network-based simulators
We explored a novel framework for simulating the small-scale processes that drive the evolution of aerosol, cloud, and precipitation particles, which are a critical gap in the predictive understanding of weather and climate. Particle-based methods have emerged as an effective tool for modeling aerosol-cloud-precipitation interactions, but existing particle-based models are computationally too expensive to simulate the large domains relevant for the atmosphere or to represent the full suite of relevant processes. The lack of a comprehensive and efficient reference model is a critical bottleneck in our understanding of cloud and precipitation processes and our ability to parameterize these processes for regional- and global-scale simulations. To address this need, we explored an approach to accelerate and expand particle-based models using a new machine learning approach, graph network-based simulators (GNS). Rather than modeling the evolution of the system by numerically integrating continuity equations, the GNS represents dynamics through learned message passing. Our aim was to develop fast and accurate surrogate models for particle-based simulations. We explored applying GNS to simulate cloud droplet transport, growth, and evaporation under turbulent conditions, but we found the GNS over-smoothed the simulations. We then applied the GNS to simulate aerosol dynamics through gas condensation and found the GNS was able to reproduce the benchmark, physics-based simulation with high accuracy.
54 ENVIRONMENTAL SCIENCES↗
Efficient Hybrid Attack Graph Generation for Cyber-Physical System Resilience Experimentation (Final Project Report)
HAGEN project has developed theory, algorithms, and capabilities to assist cyber physical system modelers and operators to perform system and device-level vulnerability assessment, risk assessment, impact assessment, and mitigation planning. The project generates hybrid attack graphs for Cyber-Physical System (CPS) resilience experimentation at desired scale and speed. The project will produce composite attack datasets, algorithms, and demonstrable prototypical tools, and a library of high-impact attack sequences for a given CPS of interest. This report provided overall summary of research and development performed between FY22-24.
45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
On the use of Graphs for Test Sequence Selection
This report demonstrates that applying graph theory techniques provides a way to obtain sufficient statistics in finding errors when testing complex state machines. It discusses how to define the tests, then demonstrates how to automatically generate test suites that diversify test cases, subject to constraints. If included within a continuous integration approach, these constructs provide an unbiased means to systematically check for errors within the latest controller software release.
97 MATHEMATICS AND COMPUTING↗
End-to-End Optimization for Battery Materials and Molecules by Combining Graph Neural Networks and Reinforcement Learning
The National Renewable Energy Laboratory (NREL), together with the Colorado School of Mines (CSM) and Colorado State University (CSU), has developed a machine learning-enhanced approach to the design of new battery materials. Currently, such materials are designed in part via numerous expensive high-fidelity computational simulations that predict the performance of a given composition. Even with computational screening tools, the vast landscape of possible molecular or crystal structures exceeds current and future computational capacity. Improving the efficiency by which new materials can be optimized will therefore disrupt the cost, risk, and time required to bring new energy solutions to the marketplace. Predicting the properties of an organic molecule or periodic crystalline material given its structure has grown increasingly common. These approaches leverage large-scale computational and experimental databases and ML approaches such as graph neural networks. The inverse design problem of finding a material that possesses desired properties is substantially more challenging, since enumerating all valid material structures is not feasible. In this project, we leveraged recent success in reinforcement learning to efficiently navigate this high-dimensional search space. Just as algorithms can find the optimal chess moves from nearly limitless options, we train an approach to evolve a simple starting structure into a complex structure that possess the desired properties. Our solution has been demonstrated by applying it to two related design application tasks for short- and long-term energy storage, respectively: (1) the design of solid-state ion conductors and (2) the design of organic redox-active materials. The project has resulted an open-source software library for material design, documented examples of applying the library to both organic and inorganic material optimization, and peer-reviewed publications detailing the data, computational models, and resulting candidate materials.
25 ENERGY STORAGE↗
Facial Named Entity Recognition by Attention-Based Graph Convolutional Neural Network
In the realm of facial recognition and analysis, the ability to accurately cluster large datasets of facial images stands as a cornerstone for various applications, ranging from security surveillance to user biometric identification. This project evolves a novel approach to facial data clustering by embedding facial images into a high-dimensional vector space using an advanced embedding model trained on separate data and assumes a graph-like structure on the high-dimensional vectors. We find our method works significantly better than common shallow methods.
97 MATHEMATICS AND COMPUTING↗
InterGraph-CPS: A Graph-Theoretic Approach to Characterize Cross-Domain Cyber-Physical Interdependencies and Uncertainties in Electric Grid Systems for Improved Decision-Making in Operation and Response
Critical infrastructure systems such as the electric grid are increasingly cyber-physical; yet, despite the cyber-physical characteristics of critical infrastructure systems, the physical process system and communication/control network system are traditionally analyzed in siloes. As these systems become more cyber-physical, it is crucial that models and methods are available to assess the cyber physical system (CPS) interdependencies, characteristics, and event propagation for improved planning, operation, and response. Thus, we proposed an integrated structural and temporal CPS interdependency analysis framework, InterGraph-CPS, that provides insight into the CPS function during normal operation as well as disturbances. This integrated structural and temporal interdependency framework is uniquely designed for assessing CPSs by account for the challenges of analyzing cyber and physical data streams together due to data availability, data type, and time scale differences. By leveraging both structural (e.g., graph analysis) and temporal (e.g., data analytics) techniques, different CPS behaviors and configurations can be accounted for.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Bridging the Gap between Cosmological Simulations with Graph Neural Networks and Domain Adaptation
Deep learning models have been shown to outperform methods that rely on summary statistics, like the power spectrum, in extracting information from complex cosmological data sets. However, due to differences in the subgrid physics implementation and numerical approximations across different simulation suites, models trained on data from one cosmological simulation show a drop in performance when tested on another. Similarly, models trained on any of the simulations would also likely experience a drop in performance when applied to observational data. Training on data from two different suites of the CAMELS hydrodynamic cosmological simulations, we examine the generalization capabilities of Domain Adaptive Graph Neural Networks (DA-GNNs). By utilizing GNNs, we capitalize on their capacity to capture structured scale-free cosmological information from galaxy distributions. Moreover, by including unsupervised domain adaptation via Maximum Mean Discrepancy (MMD), we enable our models to extract domain-invariant features. We demonstrate that DA-GNN achieves higher accuracy and robustness on cross dataset tasks (up to 28% better relative error and up to almost an order of magnitude better χ 2 ). Using data visualizations, we show the effects of domain adaptation on proper latent space data alignment. This shows that DA-GNNs are a promising method for extracting domain-independent cosmological information, a vital step toward robust deep learning for real cosmic survey data.
97 MATHEMATICS AND COMPUTING↗
Track Reconstruction using Graph Neural Networks in the EMPHATIC Experiment
Track reconstruction is essential for extracting physics observables from detector data in high-energy and nuclear physics experiments. In this work, we investigate the use of graph neural networks (GNNs) to reconstruct particle momentum in the EMPHATIC experiment using simulated data. The model takes raw hit information from the silicon strip detectors (SSDs) as input and is trained to predict momentum components and the scattering angle of the particle. We describe the GNN architecture, training procedure, and performance metrics, and present results showing improved resolution in momentum reconstruction. These results demonstrate the potential of GNN-based approaches in track reconstruction tasks within complex detector environments like EMPHATIC.
Bhattarai, Aayush [Notre Dame U.]↗
PopGNN: Graph Neural Network-Based Flexible Future Population Forecasting Model
Accurate population forecasts is important to plan critical infrastructure and services, from housing and education to healthcare and transport. However, traditional population prediction studies have only employed traditional machine learning models limited to capture complex spatial interdependencies and patterns. Althogh recently computer vision-based framework was introduced with with promising accuracy, it has critical limitations for real-world planning applications: it function only at fixed spatial resolutions, restricting their use in diverse boundaries such as census tracts, neighborhoods, or administrative zones. Therefore, this study suggests a Graph Neural Network (GNN)-based population prediction framework, called PopGNN. This model recorded remarkable performance compared with state-of-the-art models and traditional baseline models in the grid and administrative boundaries. Furthermore, our framework achieved comparable predictive accuracy to a computer vision-based model in both the South Korea and Tennessee case studies. Consequently, this study is valuable in that a single model can provide accurate population forecasts that address diverse planning demands, ranging from granular grid-level estimates for precise service allocation and facility location planning to aggregate administrative-level forecasts for macro-scale regional policy and resource distribution.
97 MATHEMATICS AND COMPUTING↗
DG2DAG: Learning Directed Acyclic Graphs from Functional Priors
Physics-based systems-of-systems models are computationally expensive. Reduced graphical models can decrease computational complexity, but may not proffer an end-to-end model from upstream inputs to downstream outputs. We consequently are interested in reducing models on directed graphs to models on a directed acyclic subgraph such that preserves accurate reconstruction of nodes. The consequence is a model with a topological ordering, providing a one-way flow of computation, and a causal interpr
Voronin, Alexey [Sandia National Laboratories (SNL↗