1,029 research outputs found
Generalised Latent Assimilation in Heterogeneous Reduced Spaces with Machine Learning Surrogate Models
Reduced-order modelling and low-dimensional surrogate models generated using
machine learning algorithms have been widely applied in high-dimensional
dynamical systems to improve the algorithmic efficiency. In this paper, we
develop a system which combines reduced-order surrogate models with a novel
data assimilation (DA) technique used to incorporate real-time observations
from different physical spaces. We make use of local smooth surrogate functions
which link the space of encoded system variables and the one of current
observations to perform variational DA with a low computational cost. The new
system, named Generalised Latent Assimilation can benefit both the efficiency
provided by the reduced-order modelling and the accuracy of data assimilation.
A theoretical analysis of the difference between surrogate and original
assimilation cost function is also provided in this paper where an upper bound,
depending on the size of the local training set, is given. The new approach is
tested on a high-dimensional CFD application of a two-phase liquid flow with
non-linear observation operators that current Latent Assimilation methods can
not handle. Numerical results demonstrate that the proposed assimilation
approach can significantly improve the reconstruction and prediction accuracy
of the deep learning surrogate model which is nearly 1000 times faster than the
CFD simulation
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