Accelerating Graph Learning with PyTorch: Unleashing the Power of torch.compile

Discover how to enhance the performance of your PyTorch graph machine learning models by leveraging the latest advancements in torch.compile. This article dives into practical techniques that achieved...

https://news.lavx.hu/article/accelerating-graph-learning-with-pytorch-unleashing-the-power-of-torch-compile

Accelerating Graph Learning with PyTorch: Unleashing the Power of torch.compile

Discover how to enhance the performance of your PyTorch graph machine learning models by leveraging the latest advancements in torch.compile. This article dives into practical techniques that achieved...

https://news.lavx.hu/article/accelerating-graph-learning-with-pytorch-unleashing-the-power-of-torch-compile

Interpretable Physics-Informed Graph Neural Networks For Flood Forecasting
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https://doi.org/10.1111/mice.13484 <-- shared paper
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https://github.com/MehdiTaghizadehUVa/modulus/tree/main/examples/weather/flood_modeling/hydrographnet <-- shared GitHub repository
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#GIS #spatial #mapping #FloodForecasting #GraphNeuralNetworks #PhysicsInformedAI #Hydrology #ClimateResilience #ScientificMachineLearning #CivilEngineering #HydroGraphNet #PhyiscsNeMo #hydrology #water #flood #flooding #spatialanalysis #spatiotemporal #climatechange #model #modeling #machinelearning #AI #extremeweather #prediction #forecasting #planning #humanimpacts #loss #lossoflife #publicsafety #cost #economics #infrastructure #risk #hazard #disaster #mitigation #preparedness #naturaldisaster #HydroGraphNet #floodforecasting #dynamics #remotesensing #earthobservation