Van der Pol-informed Neural Networks for Multi-step-ahead Forecasting of Extreme Climatic Events
Deep learning has produced excellent results in several applied domains including computer vision, natural language processing, speech recognition, etc. Physics-informed neural networks (PINN) are a new family of deep learning models that combine prior knowledge of physics in the form of high-level...
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| Other Authors: | , , , |
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2023
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| Online Access: | https://depot.sorbonne.ae/handle/20.500.12458/1462 |
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| _version_ | 1857415063125622784 |
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| author | Dutta, Anurag |
| author2 | Panja, Madhurima Kumar, Uttam Hens, Chittaranjan Chakraborty, Tanujit |
| author2_role | author author author author |
| author_facet | Dutta, Anurag Panja, Madhurima Kumar, Uttam Hens, Chittaranjan Chakraborty, Tanujit |
| author_role | author |
| dc.creator.none.fl_str_mv | Dutta, Anurag Panja, Madhurima Kumar, Uttam Hens, Chittaranjan Chakraborty, Tanujit |
| dc.date.none.fl_str_mv | 2023 2024-01-30T09:48:19Z 2024-01-30T09:48:19Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | https://depot.sorbonne.ae/handle/20.500.12458/1462 |
| dc.language.none.fl_str_mv | en |
| dc.relation.none.fl_str_mv | 37th Conference on Neural Information Processing Systems |
| dc.title.none.fl_str_mv | Van der Pol-informed Neural Networks for Multi-step-ahead Forecasting of Extreme Climatic Events |
| dc.type.none.fl_str_mv | Controlled Vocabulary for Resource Type Genres::text::conference object::conference proceedings |
| description | Deep learning has produced excellent results in several applied domains including computer vision, natural language processing, speech recognition, etc. Physics-informed neural networks (PINN) are a new family of deep learning models that combine prior knowledge of physics in the form of high-level abstraction of natural phenomena with data-driven neural networks. PINN has emerged as a flourishing area of scientific computing to deal with the challenges of shortage of training data, enhancing physical plausibility, and specifically aiming to solve complex differential equations. However, building PINNs for modeling and forecasting the dynamics of extreme climatic events of geophysical systems remains an open scientific problem. This study proposes Van der Pol-informed Neural Networks (VPINN), a physics-informed differential learning approach, for modeling extreme nonlinear dynamical systems such as climatic events, exploiting the physical differentials as the physics-derived loss function. Our proposal is compared to state-of-the-art time series forecasting models, showing superior performance.The codes and dataset used for the experiments are made available at https: //github.com/mad-stat/VPINN. |
| id | sorbonner_61a166dcaac656121d1ad9c46a2cfde8 |
| language_invalid_str_mv | en |
| network_acronym_str | sorbonner |
| network_name_str | Sorbonne University Abu Dhabi repository |
| oai_identifier_str | oai:depot.sorbonne.ae:20.500.12458/1462 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Van der Pol-informed Neural Networks for Multi-step-ahead Forecasting of Extreme Climatic EventsDutta, AnuragPanja, MadhurimaKumar, UttamHens, ChittaranjanChakraborty, TanujitDeep learning has produced excellent results in several applied domains including computer vision, natural language processing, speech recognition, etc. Physics-informed neural networks (PINN) are a new family of deep learning models that combine prior knowledge of physics in the form of high-level abstraction of natural phenomena with data-driven neural networks. PINN has emerged as a flourishing area of scientific computing to deal with the challenges of shortage of training data, enhancing physical plausibility, and specifically aiming to solve complex differential equations. However, building PINNs for modeling and forecasting the dynamics of extreme climatic events of geophysical systems remains an open scientific problem. This study proposes Van der Pol-informed Neural Networks (VPINN), a physics-informed differential learning approach, for modeling extreme nonlinear dynamical systems such as climatic events, exploiting the physical differentials as the physics-derived loss function. Our proposal is compared to state-of-the-art time series forecasting models, showing superior performance.The codes and dataset used for the experiments are made available at https: //github.com/mad-stat/VPINN.2024-01-30T09:48:19Z2024-01-30T09:48:19Z2023Controlled Vocabulary for Resource Type Genres::text::conference object::conference proceedingsapplication/pdfhttps://depot.sorbonne.ae/handle/20.500.12458/1462en37th Conference on Neural Information Processing Systemsoai:depot.sorbonne.ae:20.500.12458/14622024-03-10T07:10:39Z |
| spellingShingle | Van der Pol-informed Neural Networks for Multi-step-ahead Forecasting of Extreme Climatic Events Dutta, Anurag |
| title | Van der Pol-informed Neural Networks for Multi-step-ahead Forecasting of Extreme Climatic Events |
| title_full | Van der Pol-informed Neural Networks for Multi-step-ahead Forecasting of Extreme Climatic Events |
| title_fullStr | Van der Pol-informed Neural Networks for Multi-step-ahead Forecasting of Extreme Climatic Events |
| title_full_unstemmed | Van der Pol-informed Neural Networks for Multi-step-ahead Forecasting of Extreme Climatic Events |
| title_short | Van der Pol-informed Neural Networks for Multi-step-ahead Forecasting of Extreme Climatic Events |
| title_sort | Van der Pol-informed Neural Networks for Multi-step-ahead Forecasting of Extreme Climatic Events |
| url | https://depot.sorbonne.ae/handle/20.500.12458/1462 |