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Physics-informed multi-task learning for permeability prediction and probabilistic HFU modeling: a case study from the Lower Bahariya Reservoir, Shahd SE field Egypt - Nature
Physics-informed multi-task learning for permeability prediction and probabilistic HFU modeling: a case study from the Lower Bahariya Reservoir, Shahd SE field Egypt Nature
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- PossiblePossibly related (embedding) · 50%SciML/ReservoirComputing.jl →
- PossiblePossibly related (embedding) · 46%DELUGE: Towards Continental-Scale Daily Pluvial Flood Damage Prediction via Interpretable Conditioning on Foundation Model Embeddings →
- PossiblePossibly related (embedding) · 45%PIER: Physics-Informed Environmental Retrieval for Time-Series Modeling →
- PossiblePossibly related (embedding) · 48%Composing Flow-Matching Energies with Known Physics: Generation, OOD Detection, and Inversion on PDE Fields →
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paperDELUGE: Towards Continental-Scale Daily Pluvial Flood Damage Prediction via Interpretable Conditioning on Foundation Model EmbeddingspaperPIER: Physics-Informed Environmental Retrieval for Time-Series ModelingpaperComposing Flow-Matching Energies with Known Physics: Generation, OOD Detection, and Inversion on PDE Fields
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repoSciML/ReservoirComputing.jlpaperComposing Flow-Matching Energies with Known Physics: Generation, OOD Detection, and Inversion on PDE FieldspaperPIER: Physics-Informed Environmental Retrieval for Time-Series ModelingpaperDELUGE: Towards Continental-Scale Daily Pluvial Flood Damage Prediction via Interpretable Conditioning on Foundation Model Embeddings