DiscreteModel#
- class DiscreteModel(operators)[source]#
Nonparametric discrete dynamical system model \(\qhat_{j+1} = \fhat(\qhat_{j}, \u_{j})\).
Here,
\(\qhat_j\in\RR^{r}\) is the \(j\)-th iteration of the model state, and
\(\u_j\in\RR^{m}\) is the (optional) corresponding input.
The structure of \(\fhat\) is specified through the
operators
attribute.- Parameters
- operatorslist of
opinf.operators
objects Operators comprising the terms of the model.
- operatorslist of
Properties
A_
opinf.operators.LinearOperator
(orNone
).B_
opinf.operators.InputOperator
(orNone
).G_
opinf.operators.CubicOperator
(orNone
).H_
opinf.operators.QuadraticOperator
(orNone
).N_
opinf.operators.StateInputOperator
(orNone
).c_
opinf.operators.ConstantOperator
(orNone
).data_matrix_
\(k \times d(r, m)\) data matrix, e.g., \(\D = [~ \mathbf{1}~~ \widehat{\Q}\trp~~ (\widehat{\Q}\odot\widehat{\Q})\trp~~ \U\trp~]\).
input_dimension
Dimension \(m\) of the input (zero if there are no inputs).
operator_matrix_
\(r \times d(r, m)\) operator matrix, e.g., \(\Ohat = [~\chat~~\Ahat~~\Hhat~~\Bhat~]\).
operator_matrix_dimension
Number of columns \(d(r, m)\) of the operator matrix \(\Ohat\) and the data matrix \(\D\), i.e., the number of unknowns in the Operator Inference regression problem for each system mode.
operators
Operators comprising the terms of the model.
state_dimension
Dimension \(r\) of the state.
Methods
Make a copy of the model.
Learn the model operators from data.
Construct a reduced-order model by taking the (Petrov-)Galerkin projection of each model operator.
Sum the state Jacobian of each model operator.
Load a serialized model from an HDF5 file, created previously from the
save()
method.Step forward the discrete dynamical system
niters
steps.Evaluate the right-hand side of the model by applying each operator and summing the results.
Serialize the model, saving it in HDF5 format.
Translate a collection of state trajectories and (optionally) inputs to arrays that are appropriate arguments for
fit()
.