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Found 7 papers, 6 papers with code
DYffusion: A Dynamics-informed Diffusion Model for Spatiotemporal Forecasting
While diffusion models can successfully generate data and make predictions, they are predominantly designed for static images.
Climate Intervention Analysis using AI Model Guided by Statistical Physics Principles
Our model, AiBEDO, is capable of capturing the complex, multi-timescale effects of radiation perturbations on global and regional surface climate, allowing for a substantial acceleration of the exploration of the impacts of spatially-heterogenous climate forcers.
ClimART: A Benchmark Dataset for Emulating Atmospheric Radiative Transfer in Weather and Climate Models
Numerical simulations of Earth's weather and climate require substantial amounts of computation.
End-to-End Weak Supervision
Aggregating multiple sources of weak supervision (WS) can ease the data-labeling bottleneck prevalent in many machine learning applications, by replacing the tedious manual collection of ground truth labels.
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Dependency Structure Misspecification in Multi-Source Weak Supervision Models
Data programming (DP) has proven to be an attractive alternative to costly hand-labeling of data.
The World as a Graph: Improving El Niño Forecasts with Graph Neural Networks
In comparison, graph neural networks (GNNs) are capable of modeling large-scale spatial dependencies and are more interpretable due to the explicit modeling of information flow through edge connections.
Graph Neural Networks for Improved El Niño Forecasting
Deep learning-based models have recently outperformed state-of-the-art seasonal forecasting models, such as for predicting El Ni\~no-Southern Oscillation (ENSO).
Multivariate Time Series Forecasting
Spatio-Temporal Forecasting
