Image de couverture de livre

IDRIFTNET: Physics-Driven Spatiotemporal Deep Learning for Iceberg Drift Forecasting

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Putatunda, Rohan, Purushotham, Sanjay, Lele, Ratnaksha, Janeja, Vandana P.
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning Atmospheric and Oceanic Physics
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

DeepSeasons: a Deep Learning scale-selecting approach to Seasonal Forecasts
par: Navarra, A., et autres
Publié: (2025)

Advancing Marine Heatwave Forecasts: An Integrated Deep Learning Approach
par: Ning, Ding, et autres
Publié: (2024)

Using Deep Learning to Identify Initial Error Sensitivity for Interpretable ENSO Forecasts
par: Toride, Kinya, et autres
Publié: (2024)

Validating Deep Learning Weather Forecast Models on Recent High-Impact Extreme Events
par: Pasche, Olivier C., et autres
Publié: (2024)

Spatiotemporal Forecasting in Climate Data Using EOFs and Machine Learning Models: A Case Study in Chile
par: Herrera, Mauricio, et autres
Publié: (2025)

DUNE: A Machine Learning Deep UNet++ based Ensemble Approach to Monthly, Seasonal and Annual Climate Forecasting
par: Shukla, Pratik, et autres
Publié: (2024)

Towards Physically Consistent Deep Learning For Climate Model Parameterizations
par: Kühbacher, Birgit, et autres
Publié: (2024)

MMWSTM-ADRAN+: A Novel Hybrid Deep Learning Architecture for Enhanced Climate Time Series Forecasting and Extreme Event Prediction
par: Ahmed, Shaheen Mohammed Saleh, et autres
Publié: (2025)

DaYu: Data-Driven Model for Geostationary Satellite Observed Cloud Images Forecasting
par: Wei, Xujun, et autres
Publié: (2024)

Data Driven Deep Learning for Correcting Global Climate Model Projections of SST and DSL in the Bay of Bengal
par: Pasula, Abhishek, et autres
Publié: (2025)

Difference Learning for Air Quality Forecasting Transport Emulation
par: Chen, Reed River, et autres
Publié: (2024)

Machine Learning for Cloud Detection in IASI Measurements: A Data-Driven SVM Approach with Physical Constraints
par: Zugarini, Chiara, et autres
Publié: (2025)

Towards Location-Specific Precipitation Projections Using Deep Neural Networks
par: Kumar, Bipin, et autres
Publié: (2025)

Machine Learning Techniques for Data Reduction of Climate Applications
par: Li, Xiao, et autres
Publié: (2024)

Learning to Advect: A Neural Semi-Lagrangian Architecture for Weather Forecasting
par: Pereira, Carlos A., et autres
Publié: (2026)

A Generative Framework for Probabilistic, Spatiotemporally Coherent Downscaling of Climate Simulation
par: Schmidt, Jonathan, et autres
Publié: (2024)

Geographic Transferability of Machine Learning Models for Short-Term Airport Fog Forecasting
par: Castillo, Marcelo Cerda
Publié: (2025)

Site-specific Deterministic Temperature and Humidity Forecasts with Explainable and Reliable Machine Learning
par: Han, MengMeng, et autres
Publié: (2024)

OneForecast: A Universal Framework for Global and Regional Weather Forecasting
par: Gao, Yuan, et autres
Publié: (2025)

Diving Deep: Forecasting Sea Surface Temperatures and Anomalies
par: Ning, Ding, et autres
Publié: (2025)

An Adaptive Spatiotemporal Clustering Framework for 3D Ocean Subsurface Temperature Reconstruction
par: Loo, Ming Shan, et autres
Publié: (2026)

Graph Neural Networks and Spatial Information Learning for Post-Processing Ensemble Weather Forecasts
par: Feik, Moritz, et autres
Publié: (2024)

Examining Fast Radiatively Driven Responses Using Machine-Learning Weather Emulators
par: Mahesh, Ankur, et autres
Publié: (2026)

Longwang: Zero-Shot Global Spatiotemporal Precipitation Downscaling with a Latent Generative Prior
par: Wang, Yue, et autres
Publié: (2026)

Building Machine Learning Limited Area Models: Kilometer-Scale Weather Forecasting in Realistic Settings
par: Adamov, Simon, et autres
Publié: (2025)

Hard-Constrained Deep Learning for Climate Downscaling
par: Harder, Paula, et autres
Publié: (2022)

Data-Driven Integration Kernels for Interpretable Nonlocal Operator Learning
par: Ferretti, Savannah L., et autres
Publié: (2026)

Do Tensorized Large-Scale Spatiotemporal Dynamic Atmospheric Data Exhibit Low-Rank Properties?
par: Solgi, Ryan, et autres
Publié: (2025)

Storm Surge Modeling in the AI ERA: Using LSTM-based Machine Learning for Enhancing Forecasting Accuracy
par: Giaremis, Stefanos, et autres
Publié: (2024)

A Physics-Constrained Neural Differential Equation Framework for Data-Driven Snowpack Simulation
par: Charbonneau, Andrew, et autres
Publié: (2024)

Ocean Wave Forecasting with Deep Learning as Alternative to Conventional Models
par: Zhang, Ziliang, et autres
Publié: (2024)

Oya: Deep Learning for Accurate Global Precipitation Estimation
par: Brempong, Emmanuel Asiedu, et autres
Publié: (2025)

Deep Learning Models for Flood Predictions in South Florida
par: Shi, Jimeng, et autres
Publié: (2023)

Super Resolution On Global Weather Forecasts
par: Zhang, Lawrence, et autres
Publié: (2024)

Leadsee-Precip: A Deep Learning Diagnostic Model for Precipitation
par: Ji, Weiwen, et autres
Publié: (2024)

PEAR: Equal Area Weather Forecasting on the Sphere
par: Linander, Hampus, et autres
Publié: (2025)

Continuous Ensemble Weather Forecasting with Diffusion models
par: Andrae, Martin, et autres
Publié: (2024)

Forecasting Tropical Cyclones with Cascaded Diffusion Models
par: Nath, Pritthijit, et autres
Publié: (2023)

Guided Diffusion Sampling for Precipitation Forecast Interventions
par: Ueyama, Ayumu, et autres
Publié: (2026)

WindDragon: Enhancing wind power forecasting with Automated Deep Learning
par: Keisler, Julie, et autres
Publié: (2024)