{"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-advection","title":"Climate modeling with neural advection–diffusion equation","arxiv_id":null,"date":"2023-01-31","proceeding":"Knowledge and Information Systems 2023 1","authors":["Hwangyong Choi","Jeongwhan Choi","Jeehyun Hwang","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 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.","url_abs":"https://link.springer.com/article/10.1007/s10115-023-01829-2","url_pdf":"https://link.springer.com/content/pdf/10.1007/s10115-023-01829-2.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-advection","repo_url":"https://github.com/jeongwhanchoi/NADE","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"climate-modeling-with-neural-advection","repo_url":"https://github.com/hwangyong753/NADE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"weather-forecasting","task_name":"Weather Forecasting"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/weather-forecasting-on-la","task":"Weather Forecasting","dataset":"LA","model":"NADE","rank_in_archive_order":1,"of":4,"metrics":{"MSE (t+1)":"0.1415 ± 0.0213","MSE (t+6)":"0.7195 ± 0.0575"},"uses_additional_data":false},{"leaderboard":"/sota/weather-forecasting-on-noaa-atmospheric","task":"Weather Forecasting","dataset":"NOAA Atmospheric Temperature Dataset","model":"NADE","rank_in_archive_order":1,"of":5,"metrics":{"MAE (t+1)":"0.2571 ± 0.0064","MAE (t+10)":"1.3492 ± 0.0988"},"uses_additional_data":false},{"leaderboard":"/sota/weather-forecasting-on-sd","task":"Weather Forecasting","dataset":"SD","model":"NADE","rank_in_archive_order":1,"of":10,"metrics":{"MSE (t+1)":"0.1430 ± 0.0280","MSE (t+6)":"0.6516 ± 0.0657"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}