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- UrbanFloodCast Public
Urban flood modeling and forecasting with deep neural operator and transfer learning (https://doi.org/10.1016/j.jhydrol.2025.133705)
HydroPML/UrbanFloodCast’s past year of commit activity - Dataset4HydroPML Public
HydroPML/Dataset4HydroPML’s past year of commit activity - FloodCast Public
Large-scale flood modeling and forecasting with FloodCast (https://doi.org/10.1016/j.watres.2024.122162)
HydroPML/FloodCast’s past year of commit activity - Landslidecast Public
HydroPML for landslide dynamic process modeling and forecast (https://doi.org/10.1029/2023EA003417)
HydroPML/Landslidecast’s past year of commit activity - PaML Public
Physics-aware ML (PaML) aims to take the best from both physics-based modeling and state-of-the-art ML models to better solve scientific problems (https://arxiv.org/abs/2310.05227)
HydroPML/PaML’s past year of commit activity - PaML_PDgML Public
Physical Data-guided Machine Learning (PDgML) is a supervised DL model that statistically learns the known or unknown physics of a desired phenomenon by extracting features or attributes from raw training data.
HydroPML/PaML_PDgML’s past year of commit activity - PaML_PiML Public
PiML is a widely used approaches to incorporate physical constraints, which can be trained from additional information obtained by enforcing the physical laws (for example, designing loss functions (regularization))
HydroPML/PaML_PiML’s past year of commit activity
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