Data Assimilation with Machine Learning for Dynamical Systems: Modelling Indoor Ventilation
Résumé
Data assimilation is a method of combining physical observations with prior knowledge (for instance, a computational simulation) in order to produce an improved model; that is, improved over what the
physical observations or the computational simulation could offer in isolation. 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.
In this paper we propose an approach that uses a non-intrusive reduced-order model (NIROM) as a surrogate for a high-resolution model thereby saving computational effort. The mismatch between observations and the surrogate model is propagated forwards and backwards in time in a manner similar to 4D-variational data assimilation methods. The observations and prior are reconciled in a new way which takes full advantage of the neural network used in the NIROM and also means that there is no need to form the sensitivities explicitly when propagating the mismatch. Instead, the observations are part of the input and output of the network.
Modelling the air quality in a school classroom is the test case for our demonstration. Firstly, the data assimilation approach is shown to perform very well in a dual-twin type experiment, and secondly, the
approach is used to assimilate observations collected from a classroom in Houndsfield Primary School with predictions from the NIROM.
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