Climate modeling with neural advection–diffusion equation

Hwangyong Choi, Jeongwhan Choi, Jeehyun Hwang, Kookjin Lee, Dongeun Lee, Noseong Park

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


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 two concepts, the neural ordinary differential equation (NODE) and the advection–diffusion equation. The advection–diffusion equation is widely used for climate modeling because it describes many physical processes involving Brownian and bulk motions in climate systems. On the other hand, 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 advection–diffusion equation (NADE). Our NADE, equipped with the advection–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 three real-world and two synthetic datasets and fourteen baselines, our method consistently outperforms existing baselines by non-trivial margins.

Original languageEnglish
Pages (from-to)2403-2427
Number of pages25
JournalKnowledge and Information Systems
Issue number6
Publication statusPublished - 2023 Jun

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems
  • Human-Computer Interaction
  • Hardware and Architecture
  • Artificial Intelligence


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