Data assimilation with machine learning for dynamical systems: Modelling indoor ventilation

Claire E. Heaney, Jieyi Tang, Jintao Yan, Donghu Guo, Jamesson Ipock, Sanjana Kaluvakollu, Yushen Lin, Danhui Shao, Boyang Chen, Laetitia Mottet, Prashant Kumar and Christopher C. Pain

Physica A: Statistical Mechanics and its Applications, 2024, vol. 643, issue C

Abstract: Data assimilation is a method of combining physical observations with prior knowledge (for instance, a computational simulation) in order to produce an improved estimate of the state of a system; that is, improved over what the physical observations or the computational simulation, alone, could offer. Recently, machine learning techniques have been deployed in order to address the significant computational burden that is associated with the procedures involved in data assimilation.

Keywords: Machine learning; Data assimilation; Adversarial neural network; Indoor fluid dynamics modelling (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:643:y:2024:i:c:s0378437124002929

DOI: 10.1016/j.physa.2024.129783

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