Multi-domain encoder–decoder neural networks for latent data assimilation in dynamical systems
High-dimensional dynamical systems often require computationally intensive physics-based simulations, making full physical space data assimilation impractical. Latent data assimilation methods perform assimilation in reduced-order latent space for efficiency but struggle with complex, nonlinear stat...
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| Vydané v: | Computer methods in applied mechanics and engineering Ročník 430; s. 117201 |
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| Hlavní autori: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier B.V
01.10.2024
Elsevier |
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| ISSN: | 0045-7825, 1879-2138 |
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| Abstract | High-dimensional dynamical systems often require computationally intensive physics-based simulations, making full physical space data assimilation impractical. Latent data assimilation methods perform assimilation in reduced-order latent space for efficiency but struggle with complex, nonlinear state-observation mappings. Recent solutions like Generalized Latent Data Assimilation (GLA) and Latent Space Data Assimilation (LSDA) address heterogeneous latent spaces by incorporating surrogate mapping functions but introduce computational costs and uncertainties. Furthermore, current algorithms that integrate data assimilation and deep learning still face limitations when it comes to handling non-explicit mapping functions. To address these challenges, this paper introduces a novel deep-learning-based data assimilation scheme, named Multi-domain Encoder–Decoder Latent Data Assimilation (MEDLA), capable of handling diverse data sources by sharing a common latent space. The proposed approach significantly reduces the computational burden since the complex mapping functions are mimicked by the multi-domain encoder–decoder neural network. It also enhances assimilation accuracy by minimizing interpolation and approximation errors. Extensive numerical experiments from three different test cases assess MEDLA’s performance in high dimensional dynamical systems, benchmarking it against state-of-the-art latent data assimilation methods. The numerical results consistently underscore MEDLA’s superiority in managing multi-scale observational data and tackling intricate, non-explicit mapping functions. |
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| AbstractList | High-dimensional dynamical systems often require computationally intensive physics-based simulations, making full physical space data assimilation impractical. Latent data assimilation methods perform assimilation in reduced-order latent space for efficiency but struggle with complex, nonlinear state-observation mappings. Recent solutions like Generalized Latent Data Assimilation (GLA) and Latent Space Data Assimilation (LSDA) address heterogeneous latent spaces by incorporating surrogate mapping functions but introduce computational costs and uncertainties. Furthermore, current algorithms that integrate data assimilation and deep learning still face limitations when it comes to handling non-explicit mapping functions. To address these challenges, this paper introduces a novel deep-learning-based data assimilation scheme, named Multi-domain Encoder–Decoder Latent Data Assimilation (MEDLA), capable of handling diverse data sources by sharing a common latent space. The proposed approach significantly reduces the computational burden since the complex mapping functions are mimicked by the multi-domain encoder–decoder neural network. It also enhances assimilation accuracy by minimizing interpolation and approximation errors. Extensive numerical experiments from three different test cases assess MEDLA’s performance in high dimensional dynamical systems, benchmarking it against state-of-the-art latent data assimilation methods. The numerical results consistently underscore MEDLA’s superiority in managing multi-scale observational data and tackling intricate, non-explicit mapping functions. |
| ArticleNumber | 117201 |
| Author | Zhuang, Yilin Cheng, Sibo Kahouadji, Lyes Arcucci, Rossella Matar, Omar K. Chen, Jianhua Liu, Che |
| Author_xml | – sequence: 1 givenname: Sibo orcidid: 0000-0002-8707-2589 surname: Cheng fullname: Cheng, Sibo email: sibo.cheng@enpc.fr organization: CEREA, École des Ponts and EDF R&D, Île-de-France, France – sequence: 2 givenname: Yilin orcidid: 0000-0002-4959-1847 surname: Zhuang fullname: Zhuang, Yilin organization: Department of chemical engineering, Imperial College London, UK – sequence: 3 givenname: Lyes orcidid: 0000-0001-8805-1192 surname: Kahouadji fullname: Kahouadji, Lyes organization: Department of chemical engineering, Imperial College London, UK – sequence: 4 givenname: Che surname: Liu fullname: Liu, Che organization: Department of Earth Science & Engineering, Imperial College London, UK – sequence: 5 givenname: Jianhua surname: Chen fullname: Chen, Jianhua organization: Department of chemical engineering, Imperial College London, UK – sequence: 6 givenname: Omar K. orcidid: 0000-0002-0530-8317 surname: Matar fullname: Matar, Omar K. organization: Department of chemical engineering, Imperial College London, UK – sequence: 7 givenname: Rossella orcidid: 0000-0002-9471-0585 surname: Arcucci fullname: Arcucci, Rossella organization: Department of Earth Science & Engineering, Imperial College London, UK |
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| Keywords | Deep learning Data fusion Dynamical systems Data assimilation Data assimilation Deep learning Data fusion Dynamical systems |
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