Abstract
In this paper, we propose Deep Data Assimilation (DDA), an integration of Data Assimilation (DA) with Machine Learning (ML). DA is the Bayesian approximation of the true state of some physical system at a given time by combining time-distributed observations with a dynamic model in an optimal way. We use a ML model in order to learn the assimilation process. In particular, a recurrent neural network, trained with the state of the dynamical system and the results of the DA process, is applied for this purpose. At each iteration, we learn a function that accumulates the misfit between the results of the forecasting model and the results of the DA. Subsequently, we compose this function with the dynamic model. This resulting composition is a dynamic model that includes the features of the DA process and that can be used for future prediction without the necessity of the DA. In fact, we prove that the DDA approach implies a reduction of the model error, which decreases at each iteration; this is achieved thanks to the use of DA in the training process. DDA is very useful in that cases when observations are not available for some time steps and DA cannot be applied to reduce the model error. The effectiveness of this method is validated by examples and a sensitivity study. In this paper, the DDA technology is applied to two different applications: the Double integral mass dot system and the Lorenz system. However, the algorithm and numerical methods that are proposed in this work can be applied to other physics problems that involve other equations and/or state variables.
Highlights
Introduction and MotivationsThe present work is placed in the context of the design of reliable algorithms for solving Data Assimilation (DA) for dynamic systems applications
We introduce a sensitivity analysis that is based on the backward error analysis to compare the error in Deep Data Assimilation (DDA) result and the error in DA
We prove that the error in the results obtained by DDA is reduced with respect the DA
Summary
The present work is placed in the context of the design of reliable algorithms for solving Data Assimilation (DA) for dynamic systems applications. Given the numerical problem, an algorithm is developed and implemented as a mathematical software At this stage, the inevitable rounding errors introduced by working in finite-precision arithmetic occur. In real case scenarios (i.e., in some operational centres), data driven models are welcome to support Computational Fluid Dynamics simulations [9,10,22], but the systems of Partial Differential Equations that are stably implemented to predict (weather, climate, ocean, air pollution, et al.) dynamics are not replaced due to the big approximations that this replacement would introduce. These methods are especially suited to systems involving Gaussian process, as presented in [24,25,26] These are developed data-driven algorithms that are capable, under a unified approach, of learning nonlinear, space-dependent cross-correlations, and of estimating model statistics.
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