Generalised latent assimilation in heterogeneous reduced spaces with machine learning surrogate models
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Publication:2111169
DOI10.1007/s10915-022-02059-4OpenAlexW4311728648WikidataQ117819973 ScholiaQ117819973MaRDI QIDQ2111169
Sibo Cheng, Charitos Anastasiou, Omar K. Matar, Rossella Arcucci, Panagiota Angeli, Jian-Hua Chen, Christopher C. Pain, Yi Ke Guo
Publication date: 23 December 2022
Published in: Journal of Scientific Computing (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2204.03497
Artificial neural networks and deep learning (68T07) Mathematical modeling or simulation for problems pertaining to fluid mechanics (76-10)
Related Items (4)
Accelerating inverse inference of ensemble Kalman filter via reduced-order model trained using adaptive sparse observations ⋮ Multi-fidelity physics constrained neural networks for dynamical systems ⋮ Blending machine learning and sequential data assimilation over latent spaces for surrogate modeling of Boussinesq systems ⋮ Efficient deep data assimilation with sparse observations and time-varying sensors
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