Climate Modeling with Neural Diffusion Equations

2021/11/11 by JeeHyun Hwang, Hwang, Jeehyun, Jeongwhan Choi +9 · 3 citations
Computer Science · Environmental Science · Physics and Astronomy · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Hydrological Forecasting Using AI #Machine Learning (cs.LG) #Model Reduction and Neural Networks

paper · pdf · doi:10.48550/arxiv.2111.06011

openalex publication_date 2021/11/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Owing to the remarkable development of deep learning technology, there have been a series of efforts to build deep learning-based climate models. Whereas most of them utilize recurrent neural networks and/or graph neural networks, we design a novel climate model based on the two concepts, the neural ordinary differential equation (NODE) and the diffusion equation. Many physical processes involving a Brownian motion of particles can be described by the diffusion equation and as a result, it is widely used for modeling climate. On the other hand, neural ordinary differential equations (NODEs) are to learn a latent governing equation of ODE from data. In our presented method, we combine them into a single framework and propose a concept, called neural diffusion equation (NDE). Our NDE, equipped with the diffusion equation and one more additional neural network to model inherent uncertainty, can learn an appropriate latent governing equation that best describes a given climate dataset. In our experiments with two real-world and one synthetic datasets and eleven baselines, our method consistently outperforms existing baselines by non-trivial margins.

Citations

• Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
• Application of Deep Convolutional Neural Networks for Detecting Extreme Weather in Climate Datasets
• Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning
• Lagrangian Neural Networks
• Dissecting the Diffusion Process in Linear Graph Convolutional Networks

Cited by

• SSTODE: Ocean-Atmosphere Physics-Informed Neural ODEs for Sea Surface Temperature Prediction
• A Causality-Aware Spatiotemporal Model for Multi-Region and Multi-Pollutant Air Quality Forecasting
• Generalising Traffic Forecasting to Regions without Traffic Observations

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• Interpretable Climate Change Modeling With Progressive Cascade Networks
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