Copertina

UniExtreme: A Universal Foundation Model for Extreme Weather Forecasting

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Ni, Hang, Zhang, Weijia, Liu, Hao
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

ExtremeCast: Boosting Extreme Value Prediction for Global Weather Forecast
di: Xu, Wanghan, et al.
Pubblicazione: (2024)

Target Concept Tuning Improves Extreme Weather Forecasting
di: Ren, Shijie, et al.
Pubblicazione: (2026)

Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Models
di: Zhang, Weijia, et al.
Pubblicazione: (2024)

RealBench: Benchmarking Data-Driven Numerical Weather Forecasting Under Operational Conditions and Extreme Event Challenges
di: Li, Ruize, et al.
Pubblicazione: (2026)

Physics-Informed Teleconnection-Aware Transformer for Global Subseasonal-to-Seasonal Forecasting
di: Lyu, Tengfei, et al.
Pubblicazione: (2025)

FEAT: A Linear-Complexity Foundation Model for Extremely Large Structured Data
di: Song, Zhenghang, et al.
Pubblicazione: (2026)

ExEBench: Benchmarking Foundation Models on Extreme Earth Events
di: Zhao, Shan, et al.
Pubblicazione: (2025)

UniDEC : Unified Dual Encoder and Classifier Training for Extreme Multi-Label Classification
di: Kharbanda, Siddhant, et al.
Pubblicazione: (2024)

Integrating Weather Foundation Model and Satellite to Enable Fine-Grained Solar Irradiance Forecasting
di: Ma, Ziqing, et al.
Pubblicazione: (2026)

Interpretable Physics-Informed Load Forecasting for U.S. Grid Resilience: SHAP-Guided Ensemble Validation in Hybrid Deep Learning Under Extreme Weather
di: Abubakkar, Md, et al.
Pubblicazione: (2026)

AutoSTF: Decoupled Neural Architecture Search for Cost-Effective Automated Spatio-Temporal Forecasting
di: Lyu, Tengfei, et al.
Pubblicazione: (2024)

A Hybrid Autoencoder-Transformer Model for Robust Day-Ahead Electricity Price Forecasting under Extreme Conditions
di: Tang, Boyan, et al.
Pubblicazione: (2025)

UniTabE: A Universal Pretraining Protocol for Tabular Foundation Model in Data Science
di: Yang, Yazheng, et al.
Pubblicazione: (2023)

A Mamba Foundation Model for Time Series Forecasting
di: Ma, Haoyu, et al.
Pubblicazione: (2024)

Skillful Subseasonal-to-Seasonal Forecasting of Extreme Events with a Multi-Sphere Coupled Probabilistic Model
di: Mu, Bin, et al.
Pubblicazione: (2025)

PFformer: A Position-Free Transformer Variant for Extreme-Adaptive Multivariate Time Series Forecasting
di: Li, Yanhong, et al.
Pubblicazione: (2025)

Generalizing Weather Forecast to Fine-grained Temporal Scales via Physics-AI Hybrid Modeling
di: Xu, Wanghan, et al.
Pubblicazione: (2024)

Multi-Quantile Regression for Extreme Precipitation Downscaling
di: Najafi, Hamed, et al.
Pubblicazione: (2026)

Extreme Region Policy Distillation
di: Chen, Changyu, et al.
Pubblicazione: (2026)

UniCA: Unified Covariate Adaptation for Time Series Foundation Model
di: Han, Lu, et al.
Pubblicazione: (2025)

Downscaling Extreme Precipitation with Wasserstein Regularized Diffusion
di: Liu, Yuhao, et al.
Pubblicazione: (2024)

UniMAP: Universal SMILES-Graph Representation Learning
di: Feng, Shikun, et al.
Pubblicazione: (2023)

Automated Spatio-Temporal Weather Modeling for Load Forecasting
di: Keisler, Julie, et al.
Pubblicazione: (2024)

Transferable and Forecastable User Targeting Foundation Model
di: Dou, Bin, et al.
Pubblicazione: (2024)

ARROW: An Adaptive Rollout and Routing Method for Global Weather Forecasting
di: Tian, Jindong, et al.
Pubblicazione: (2025)

Graph Ranking Contrastive Learning: A Extremely Simple yet Efficient Method
di: Hu, Yulan, et al.
Pubblicazione: (2023)

ClimaEmpact: Domain-Aligned Small Language Models and Datasets for Extreme Weather Analytics
di: Varshney, Deeksha, et al.
Pubblicazione: (2025)

Mitigating Time Discretization Challenges with WeatherODE: A Sandwich Physics-Driven Neural ODE for Weather Forecasting
di: Liu, Peiyuan, et al.
Pubblicazione: (2024)

XXLTraffic: Expanding and Extremely Long Traffic forecasting beyond test adaptation
di: Yin, Du, et al.
Pubblicazione: (2024)

Towards Universal Large-Scale Foundational Model for Natural Gas Demand Forecasting
di: Zhou, Xinxing, et al.
Pubblicazione: (2024)

Extreme Precipitation Nowcasting using Transformer-based Generative Models
di: Meo, Cristian, et al.
Pubblicazione: (2024)

ERATTA: Extreme RAG for Table To Answers with Large Language Models
di: Roychowdhury, Sohini, et al.
Pubblicazione: (2024)

PhyDA: Physics-Guided Diffusion Models for Data Assimilation in Atmospheric Systems
di: Wang, Hao, et al.
Pubblicazione: (2025)

From Consistency to Complementarity: Aligned and Disentangled Multi-modal Learning for Time Series Understanding and Reasoning
di: Ni, Hang, et al.
Pubblicazione: (2026)

Uni-Mol2: Exploring Molecular Pretraining Model at Scale
di: Ji, Xiaohong, et al.
Pubblicazione: (2024)

UniTST: Effectively Modeling Inter-Series and Intra-Series Dependencies for Multivariate Time Series Forecasting
di: Liu, Juncheng, et al.
Pubblicazione: (2024)

Uni-NTFM: A Unified Foundation Model for EEG Signal Representation Learning
di: Chen, Zhisheng, et al.
Pubblicazione: (2025)

HR-Extreme: A High-Resolution Dataset for Extreme Weather Forecasting
di: Ran, Nian, et al.
Pubblicazione: (2024)

ClimateLLM: Efficient Weather Forecasting via Frequency-Aware Large Language Models
di: Li, Shixuan, et al.
Pubblicazione: (2025)

Irregular Traffic Time Series Forecasting Based on Asynchronous Spatio-Temporal Graph Convolutional Network
di: Zhang, Weijia, et al.
Pubblicazione: (2023)