simpeg.potential_fields.gravity.Simulation3DDifferential.Jtvec#
- Simulation3DDifferential.Jtvec(m, v, f=None)[source]#
Compute the Jacobian transpose times a vector for the model provided.
The Jacobian defines the derivative of the predicted data vector with respect to the model parameters. For a data vector \(\mathbf{d}\) predicted for a set of model parameters \(\mathbf{m}\), the Jacobian is an
(n_data, n_param)matrix whose elements are given by:\[J_{ij} = \frac{\partial d_i}{\partial m_j}\]For a model m and vector v, the
Jtvecmethod computes the matrix-vector product with the adjoint-sensitivity\[\mathbf{u} = \mathbf{J^T \, v}\]- Parameters:
- m(
n_param, )numpy.ndarray The model parameters.
- v(
n_data, )numpy.ndarray Vector we are multiplying.
- f
simpeg.field.Fields,optional If provided, fields will not need to be recomputed for the current model to compute Jtvec.
- m(
- Returns:
- (
n_param, )numpy.ndarray The Jacobian transpose times a vector for the model and vector provided.
- (