{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/climate-modeling-with-neural-diffusion","title":"Climate Modeling with Neural Diffusion Equations","arxiv_id":"2111.06011","date":"2021-11-11","proceeding":null,"authors":["Jeehyun Hwang","Jeongwhan Choi","Hwangyong Choi","Kookjin Lee","Dongeun Lee","Noseong Park"],"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.","url_abs":"https://arxiv.org/abs/2111.06011v1","url_pdf":"https://arxiv.org/pdf/2111.06011v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"climate-modeling-with-neural-diffusion","repo_url":"https://github.com/jeehyunhwang/neural-diffusion-equation","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"climate-modeling-with-neural-diffusion","repo_url":"https://github.com/jeongwhanchoi/Neural-Diffusion-Equation","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"weather-forecasting","task_name":"Weather Forecasting"}],"methods":[{"method_slug":"diffusion","method_name":"Diffusion"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/weather-forecasting-on-la","task":"Weather Forecasting","dataset":"LA","model":"NDE","rank_in_archive_order":3,"of":4,"metrics":{"MSE (t+1)":"0.2621 ± 0.0026","MSE (t+6)":"0.7594 ± 0.0225"},"uses_additional_data":false},{"leaderboard":"/sota/weather-forecasting-on-noaa-atmospheric","task":"Weather Forecasting","dataset":"NOAA Atmospheric Temperature Dataset","model":"NDE","rank_in_archive_order":2,"of":5,"metrics":{"MAE (t+1)":"0.2975 ± 0.0062","MAE (t+10)":"1.6337 ± 0.0467"},"uses_additional_data":false},{"leaderboard":"/sota/weather-forecasting-on-sd","task":"Weather Forecasting","dataset":"SD","model":"NDE","rank_in_archive_order":3,"of":10,"metrics":{"MSE (t+1)":"0.3561 ± 0.0055","MSE (t+6)":"0.7301 ± 0.0048"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2111.06011","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.06011"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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