Sentence examples for model state vector from inspiring English sources

As such, the model state vector, U, is augmented to include the dynamic state variables and the time varying water injection and oil production rates: U = left[ {Vquad Q} right]^{text{T}} (9 where V consists of the water saturation for each grid block in the numerical solution scheme and Q represents the respective water injection and oil production rates.

Let U be the matrix holding the ensemble members (U_{i} in R^{n},) U = (U_{1},U_{2}, ldots, U_{N} ) in R^{n times N} (10 where N is the number of ensemble members, and n is the size of the model state vector.

The Kalman filter in turn relies on an accurate characterization of the forecast uncertainty, i.e., the covariance matrix associated with the model state vector.

The data assimilation procedure updates the background in light of the new observations to produce an analysis, which, under suitable assumptions, is the maximum likelihood estimate of the model state vector.

Equation (11) shows that it is possible to compute updated maximum-likelihood estimates of all components of the model state vector, even if they cannot all be measured, provided that the observations are reasonably correlated with the model state.

Whenever measurements are available, the model state vectors are updated using the Kalman filter equations.

As with the model state vectors, we let y ¯ b be the mean of the vectors h b i, i = 1, 2,..., k and define the ℓ × k matrix Y b whose ith column is h b i - y ¯ b.

In the BOT model, the state vector include four state variables, i.e., x k = P x k P y k V x k V y k T. Following the Cartesian coordinate, the P x and P y stand for the two-dimensional position, while V x and V y are the two-dimensional velocity.

We introduce a state-space model with latent state vector modeling all relevant information of the unknown system.

In a model neuron the state vector's dimensionality can be high, and obviously it depends upon the specific model considered, making the comparison between different neuron models difficult and somewhat arbitrary.

Based on a concise review of Bayesian networks, we introduced a state-space model with latent state vector capturing all relevant information of the unknown system.