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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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Observation of a rare beta decay of the charmed baryon with a Graph Neural Network
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Predicting magnetic properties of van der Waals magnets using graph neural networks
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A Graph Neural Network for Reconstruction of LArTPC Detector Data
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Enabling Real-Time Communications in Multi-Agent Systems: A Graph Neural Network Based Approach
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Graph Neural Networks for 3D Geometry-Agnostic Predictions of Material Behavior
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A New Bi-Partite Graph Representation of 3D Solids for Geometric Reasoning and Graph Neural Networks
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Pattern Mining in Graphs via Graph Neural Networks (GNNs)
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Anomaly Detection in the SBND Experiment Based on Graph Neural Networks
Traditional anomaly detection in SBND experiments require data reconstruction and manual supervision, and thus has the drawbacks of long detection time, being labour intensive and incapable of predicting potential future anomalies. Machine learning models, especially autoencoders, have been widely applied in anomaly detection, and developing an autoencoder for anomaly detection in SBND experiment is going to tremendously improve the efficiency and accuracy of the experiment. The autoencoder has the advantage of automation, efficiency, and can be used to predict future anomalies in the SBND experiment.
Direct prediction of quantum circuit outcome probabilities using physics-aware graph neural networks
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Direct prediction of quantum circuit outcome probabilities using physics-aware graph neural networks
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Graph Neural Network Models of Multiphase Flow from Boundary Integral Methods
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Predicting the Functional State of Protein Kinases Using Interpretable Graph Neural Networks
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Interpretable Graph Neural Networks for Predicting the Functional State of Protein Kinases
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Predicting Stellar Masses of the First Galaxies Using Graph Neural Networks
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Street-level temperature estimation using graph neural networks: Performance, feature embedding and interpretability
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Predicting Lattice Vibrational Frequencies Using Deep Graph Neural Networks
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