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Gaussian Process Regression

Group /input/model/unit_XXX/particle_type_ZZZ/adsorption – ADSORPTION_MODEL = GAUSSIAN_PROCESS_REGRESSION

For information on model equations, refer to Gaussian Process Regression.

IS_KINETIC

Selects kinetic or quasi-stationary adsorption mode: 1 = kinetic, 0 = quasi-stationary. If a single value is given, the mode is set for all bound states. Otherwise, the adsorption mode is set for each bound state separately.

Type: int

Range: {0,1}

Length: 1/NTOTALBND
CP_VALS

Flattened pore-phase concentration training inputs used by the Gaussian process regression model. The values represent the training input points \(X\) used to evaluate the kernel function. The array is interpreted according to NDIM.

Unit: \(mol~m_{MP}^{-3}\)

Type: double

Range: unrestricted

Length: NTRAIN * NDIM
CS_VALS

Solid-phase training targets corresponding to CP_VALS. These values form the training output vector used to compute the GPR coefficient vector \(\alpha = (K + \sigma_n^2 I)^{-1} y\).

Unit: \(mol~m_{SP}^{-3}\)

Type: double

Range: unrestricted

Length: NTRAIN
TRAINED_PARAMS

Trained kernel hyperparameters of the Gaussian process regression model. The parameters are expected in the following order:

  • MLP weight variance

  • MLP bias variance

  • MLP variance

  • Linear variance

  • RBF variance

  • RBF lengthscale

  • Gaussian noise variance

All entries must be provided, regardless of the selected kernel.

KERNEL

Selects the kernel function used by the Gaussian process regression model. Supported values are MLP, RBF, RBF_Linear, and MLP_Linear.

Type: string

Range: {MLP, RBF, RBF_Linear, MLP_Linear}

Length: 1
NDIM

Number of input dimensions per training point used in CP_VALS. Must be a positive integer.

Type: int

Range: \(\geq 1\)

Length: 1
GPR_KKIN

Linear-driving-force coefficients in component-major ordering.

Unit: \(s^{-1}\)

Type: double

Range: \(\geq 0\)

Length: NTOTALBND