romtools.rom.qoi_surrogates#

Classes

GaussianProcessKernel([length_scale, ...])

GaussianProcessQoiModel(parameters, qois[, ...])

Gaussian Process QoI surrogate model that follows the QoiModel API.

GaussianProcessRegressorLite([kernel, ...])

class romtools.rom.qoi_surrogates.GaussianProcessKernel(length_scale: 'float' = 1.0, signal_variance: 'float' = 1.0)[source]#

Bases: object

Parameters:

  • length_scale (float)

  • signal_variance (float)

class romtools.rom.qoi_surrogates.GaussianProcessQoiModel(parameters, qois, parameter_names=None, pod_energy_fraction=0.999999, max_pod_modes=None, kernel=None, noise_variance=None, auto_noise_variance=False, noise_variance_fraction=1e-06, tune_hyperparameters=False, length_scale_grid=None, signal_variance_grid=None, normalize_parameters=False, normalize_targets=False)[source]#

Bases: object

Gaussian Process QoI surrogate model that follows the QoiModel API.

For vector QoIs, a POD is performed and a GP is trained for each reduced coefficient.

Parameters:

  • parameters (np.ndarray)

  • qois (np.ndarray)

  • parameter_names (Optional[Sequence[str]])

  • pod_energy_fraction (float)

  • max_pod_modes (Optional[int])

  • kernel (Optional[GaussianProcessKernel])

  • noise_variance (Optional[float])

  • auto_noise_variance (bool)

  • noise_variance_fraction (float)

  • tune_hyperparameters (bool)

  • length_scale_grid (Optional[Sequence[float]])

  • signal_variance_grid (Optional[Sequence[float]])

  • normalize_parameters (bool)

  • normalize_targets (bool)