GaPFlow.models.Pressure

class GaPFlow.models.Pressure(fc: Any, prop: dict, geo: dict, data: Any | None = None, gp: dict | None = None)

Bases: GaussianProcessSurrogate

Pressure model.

Supports deterministic pressure via lookup equation of state or a GP surrogate.

__init__(fc: Any, prop: dict, geo: dict, data: Any | None = None, gp: dict | None = None) → None

Constructor

Parameters:

• fc (muGrid.GlobalFieldCollection) – Field collection that provides access to ‘pressure’, ‘topography’, etc.

• prop (dict) – Physical fluid properties (e.g., shear viscosity).

• geo (dict) – Geometry parameters.

• data (Database or None, optional) – Training database if using GP surrogates.

• gp (dict or None, optional) – GP configuration dictionary (if using GP surrogates).

Methods

__init__(fc, prop, geo[, data, gp])	Constructor
build_gp(params, X, yerr)	Default GP build method.
init()	Run first training and inference.
init_database(dim)	Triggers the first database initialization.
predict([predictor, compute_var, cooldown])	Perform GP prediction, optionally updating the model via active learning (only in predictor step of the predictor-corrector time integration scheme)
save_state()	Log current GP hyperparameters and diagnostics.
update([predictor, compute_var, cooldown])	Update pressure: compute deterministic stresses and, if enabled, perform GP prediction and place predicted mean and variance into the appropriate field entries.

Attributes

Xtest	Test inputs for pressure GP (normalized).
Xtrain	Training inputs (normalized).
Xtrain_target	Training inputs (normalized).
Yerr	Observation noise (normalized) for pressure.
Yscale	Output scale for pressure (scalar-like jax.Array).
Yshift	Training outputs for pressure GP (not normalized).
Ytrain	Training outputs for pressure GP (normalized).
cumtime_infer	Cumulative time spent for inference from the GP (making predictions)
cumtime_train	Cumulative time spent for training of the GP (fitting hyperparameters)
database	The database holding the training data for the GP surrogate model.
extra	Return constant extra field, which can be used as additional input.
has_multi_output
height	Return the gap height field.
height_and_slopes	Return the topography (height and gradients).
kernel_lengthscale	Kernel lengthscale for pressure GP.
kernel_variance	Kernel variance for pressure GP.
last_fit_train_size	Size of the training database at the last fit.
name
objective	Optimization objective (negative marginal log likelihood)
obs_stddev	Observation standard deviation for pressure GP.
pressure	Pressure field.
solution	Return full solution field.
trusted	Return True if model predictive variance is below tolerance.
v_sound	Effective sound speed computed from the GP-based eos (if available) or from the analytic eos_sound_velocity.
variance	Variance of the pressure field.
is_gp_model
active_dims
use_active_learning
fix_noise
rtol
atol
tol
max_steps
pause_steps
similarity_check
allowed_skips
perturb_target
pause_on_high_residual
params_init
noise
prop
geo

property Xtest: Array

Test inputs for pressure GP (normalized).

property Xtrain: Array

Training inputs (normalized).

property Xtrain_target: Array

Training inputs (normalized).

property Yerr: float

Observation noise (normalized) for pressure.

property Yscale: Array

Output scale for pressure (scalar-like jax.Array).

property Yshift: Array

Training outputs for pressure GP (not normalized).

property Ytrain: Array

Training outputs for pressure GP (normalized).

active_dims: list[int]

allowed_skips: int

atol: float

build_gp(params: dict, X: Array, yerr: float | Array) → GaussianProcess

Default GP build method.

Parameters:

• params (dict) – Dictionary with hyperparameters

• X (jax.Array) – Input data.

• yerr (jax.Array) – Observation noise (standard deviation).

Returns:

Single-output GP model.

Return type:

tinygp.GaussianProcess

property cumtime_infer

Cumulative time spent for inference from the GP (making predictions)

property cumtime_train

Cumulative time spent for training of the GP (fitting hyperparameters)

property database

The database holding the training data for the GP surrogate model.

property extra

Return constant extra field, which can be used as additional input.

fix_noise: bool

geo: dict

property has_multi_output

property height

Return the gap height field.

property height_and_slopes: Array

Return the topography (height and gradients).

init() → None

Run first training and inference.

init_database(dim: int) → None

Triggers the first database initialization.

Parameters:

dim (int) – Dimension of the fluid problem.

is_gp_model: bool

property kernel_lengthscale: Array

Kernel lengthscale for pressure GP.

property kernel_variance: Array

Kernel variance for pressure GP.

property last_fit_train_size

Size of the training database at the last fit.

max_steps: int

name: str = 'zz'

noise: Tuple[float, float]

property objective

Optimization objective (negative marginal log likelihood)

property obs_stddev: Array

Observation standard deviation for pressure GP.

params_init: dict

pause_on_high_residual: bool

pause_steps: int

perturb_target: bool

predict(predictor: bool = True, compute_var: bool = True, cooldown: bool = False) → Tuple[Array, Array]

Perform GP prediction, optionally updating the model via active learning (only in predictor step of the predictor-corrector time integration scheme)

Parameters:

• predictor (bool, optional) – Whether to perform active learning updates (only in predictor step, default is True).

• compute_var (bool, optional) – If true (default), preditive variance is re-computed.

• cooldown (bool, optional) – If true, active learning is blocked to let the system cool down (default is False).

Returns:

• m (jax.Array) – Predictive mean.

• v (jax.Array) – Predictive variance.

property pressure: ndarray[tuple[Any, ...], dtype[floating]]

Pressure field.

prop: dict

rtol: float

save_state() → None

Log current GP hyperparameters and diagnostics.

similarity_check: bool

property solution

Return full solution field.

tol: str

property trusted: bool

Return True if model predictive variance is below tolerance.

update(predictor: bool = False, compute_var: bool = False, cooldown: bool = False) → None

Update pressure: compute deterministic stresses and, if enabled, perform GP prediction and place predicted mean and variance into the appropriate field entries.

Parameters:

• predictor (bool, optional) – Whether this update is part of the predictor stage.

• compute_var (bool, optional) – Flag for re-computing the variance (the default is False which uses the stored variance from previous steps).

• cooldown (bool, optional) – If true, active learning is blocked to let the system cool down (default is False).

use_active_learning: bool

property v_sound: ndarray[tuple[Any, ...], dtype[floating]] | Array

Effective sound speed computed from the GP-based eos (if available) or from the analytic eos_sound_velocity.

Returns:

Sound speed (may be a JAX array/scalar).

Return type:

jax.Array or scalar-like

property variance: ndarray[tuple[Any, ...], dtype[floating]]

Variance of the pressure field.