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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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Graph Convolutional Neural Networks for Modeling Materials with Microstructure.
Abstract not provided.
A Graph Theory Approach for Placing Overcurrent Relays and Reclosers for Economical Protection of Meshed Transmission Networks.
Abstract not provided.
LBAF-Viz: A New Application and Library to Visualize Computational Load and Communication Graphs.
Abstract not provided.
A Multipurpose Graph Neural Network for Reconstruction in LArTPC Detectors
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Development of crystal graph neural networks for the prediction of defect energies in calcite
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Parallel Graph Partitioning for Unstructured Meshes and Matrices
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Optimizing Thermodynamic Properties of High Entropy Alloys and Hydrides with Lattice Graph Neural Network Models
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Finding Temporal Dense Regions of Temporal Graphs
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Advancing Quantum Simulations with Machine Learning and Graph Theory
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Materials discovery for high-temperature clean-energy applications via defect graph neural networks
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Pressure-dependent thermodynamics of cubic Lu-H-N solid solutions by Monte Carlo simulations based on graph neural networks
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A new parametrization of directed acyclic graphs and causal Markov kernels for scientific feature discovery
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DynaHGraph: Learning Hidden Relationships in Dynamic Graphs
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Graph-Learning-Assisted State and Event Tracking for Solar-Penetrated Power Grids with Heterogeneous Data Sources
Unlike transmission systems, distribution systems do not typically contain sufficient metering to enable real-time state estimation. The lack of sufficient real-time measurements prohibits accurate and timely monitoring of the state of distribution systems. As a result, control and optimal operation of distribution systems, especially those containing large numbers of renewable generation units are not possible without proper data and information about the current state of the system. The main motivation of this project is to address this shortcoming by developing an approach which provides “predicted” real-time measurements so that they can be used to execute a distribution system state estimator. Thus, the objective of the project is to make the distribution systems fully observable, such that the hosting capacity for solar generation can be accurately estimated, and unnecessary solar curtailments can be avoided. In order to accomplish this goal, the project investigated the use of a grid-model-informed machine learning (ML) tool which integrates heterogeneous data streams obtained from AMI meters, SCADA as well as PMU measurements and created synchronous measurement snapshots for the state estimator (SE); and developed a hybrid robust SE which provides not only accurate state estimates but also real-time feedback for the ML model refinement.
Quantification Of Well Location and Permeability Uncertainties in Geologic CO2 Storage Using Graph Neural Operator Proxies
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Graph Attention Embeddings as a Causal Lens in Temporal Link Prediction
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Graph Attention Embeddings as a Causal Lens in Temporal Link Prediction
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