Papers › Climate modeling with neural advection–diffusion equation
Climate modeling with neural advection–diffusion equation
Hwangyong Choi, Jeongwhan Choi, Jeehyun Hwang, Kookjin Lee, Dongeun Lee, Noseong Park
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.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Weather Forecasting | LA | NADE | MSE (t+1) | 0.1415 ± 0.0213 | #1 of 4 | Archive leaderboard | report |
| Weather Forecasting | LA | NADE | MSE (t+6) | 0.7195 ± 0.0575 | #1 of 4 | Archive leaderboard | report |
| Weather Forecasting | NOAA Atmospheric Temperature Dataset | NADE | MAE (t+1) | 0.2571 ± 0.0064 | #1 of 5 | Archive leaderboard | report |
| Weather Forecasting | NOAA Atmospheric Temperature Dataset | NADE | MAE (t+10) | 1.3492 ± 0.0988 | #1 of 5 | Archive leaderboard | report |
| Weather Forecasting | SD | NADE | MSE (t+1) | 0.1430 ± 0.0280 | #1 of 10 | Archive leaderboard | report |
| Weather Forecasting | SD | NADE | MSE (t+6) | 0.6516 ± 0.0657 | #1 of 10 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections