Convolutional Lstm
PulseAugur coverage of Convolutional Lstm — every cluster mentioning Convolutional Lstm across labs, papers, and developer communities, ranked by signal.
1 day(s) with sentiment data
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Physics-guided ML improves fuel density prediction accuracy
Researchers have developed a physics-guided machine learning (PGML) framework to improve the accuracy and stability of fuel density predictions. This approach integrates physical constraints, such as mass conservation a…
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Deep neural networks enhance urban temperature forecasting with high-resolution satellite data
Researchers have developed deep neural network models to improve the resolution and forecasting of urban land surface temperatures. By combining data from geostationary and polar-orbiting satellites, they created models…
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ConvLSTM framework accurately predicts retaining wall deformation
Researchers have developed and validated a ConvLSTM framework designed to predict retaining wall deformation during staged excavation. This framework, trained on simulated data augmented with Gaussian noise, integrates …
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ML models predict reactor flow fields using CFD data
Researchers have developed a high-fidelity modeling framework combining computational fluid dynamics (CFD) with machine learning to characterize flow fields in pressurized water reactors. This approach uses physics-info…
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AI ensemble model improves retaining wall deformation forecasts
Researchers have developed a novel ensemble framework using Convolutional Long Short-Term Memory (ConvLSTM) networks to improve long-term forecasting of retaining wall deformation. This multi-resolution approach integra…
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New AI model uses 4D radar for reliable people detection
Researchers have developed a new artificial neural network architecture called TMVA4D, designed for semantic segmentation using 4D radar data. This system is intended to improve the reliability of people detection for a…
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ConvLSTM network detects gamma-ray transients for Fermi telescope
Researchers have developed a self-supervised Convolutional Long Short-Term Memory (ConvLSTM) network to detect transient gamma-ray phenomena using data from the Fermi Large Area Telescope. The framework combines end-to-…
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Deep neural networks enhance urban land surface temperature data resolution
Researchers have developed deep neural networks to improve the resolution of land surface temperature (LST) data for urban areas. By combining data from geostationary and polar-orbiting satellites, they created LST fiel…