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Found 8 papers, 1 papers with code
GraSSNet: Graph Soft Sensing Neural Networks
In the era of big data, data-driven based classification has become an essential method in smart manufacturing to guide production and optimize inspection.
Control Co-Design for Buoyancy-Controlled MHK Turbine: A Nested Optimization of Geometry and Spatial-Temporal Path Planning
Recent research progress has confirmed that using advanced controls can result in massive increases in energy capture for marine hydrokinetic (MHK) energy systems, including ocean current turbines (OCTs) and wave energy converters (WECs); however, to realize maximum benefits, the controls, power-take-off system, and basic structure of the device must all be co-designed from early stages.
ST-PCNN: Spatio-Temporal Physics-Coupled Neural Networks for Dynamics Forecasting
A spatio-temporal physics-coupled neural network (ST-PCNN) model is proposed to achieve three goals: (1) learning the underlying physics parameters, (2) transition of local information between spatio-temporal regions, and (3) forecasting future values for the dynamical system.
Physics-Coupled Spatio-Temporal Active Learning for Dynamical Systems
To tackle these challenges, we advocate a spatio-temporal physics-coupled neural networks (ST-PCNN) model to learn the underlying physics of the dynamical system and further couple the learned physics to assist the learning of the recurring dynamics.
Evolutionary Architecture Search for Graph Neural Networks
In particular, Neural Architecture Search (NAS) has seen significant attention throughout the AutoML research community, and has pushed forward the state-of-the-art in a number of neural models to address grid-like data such as texts and images.
Physics-informed Tensor-train ConvLSTM for Volumetric Velocity Forecasting of Loop Current
According to the National Academies, a weekly forecast of velocity, vertical structure, and duration of the Loop Current (LC) and its eddies is critical for understanding the oceanography and ecosystem, and for mitigating outcomes of anthropogenic and natural disasters in the Gulf of Mexico (GoM).
Multi-Label Graph Convolutional Network Representation Learning
The multi-label network nodes not only have multiple labels for each node, such labels are often highly correlated making existing methods ineffective or fail to handle such correlation for node representation learning.
Ranked #30 on
Multi-Label Classification
on MS-COCO
Feature-Attention Graph Convolutional Networks for Noise Resilient Learning
By using spectral-based graph convolution aggregation process, each node is allowed to concentrate more on the most determining neighborhood features aligned with the corresponding learning task.
