GaPFlow.models.BulkStress

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

Bases: GaussianProcessSurrogate

Bulk (gap-averaged) viscous stress model.

This model currently operates deterministically (no 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_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()	Compute and store bulk viscous stress using viscous model.

Attributes

Xtest	Test inputs.
Xtrain	Training inputs (normalized).
Xtrain_target	Training inputs (normalized).
Yerr	Observations standard error.
Yscale	Observations scaling factor.
Yshift	Observations shift.
Ytrain	Training observations (only active dimensions, scaled).
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.
dp_dx	Return ∂p/∂x.
dp_dy	Return ∂p/∂y.
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(s).
kernel_variance	Kernel variance.
last_fit_train_size	Size of the training database at the last fit.
name
objective	Optimization objective (negative marginal log likelihood)
obs_stddev	Observation noise standard deviation.
pressure	Return the pressure field.
solution	Return full solution field.
stress	Return the bulk viscous stress field.
trusted	Return True if model predictive variance is below tolerance.
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

abstract property Xtest

Test inputs.

property Xtrain: Array

Training inputs (normalized).

property Xtrain_target: Array

Training inputs (normalized).

abstract property Yerr

Observations standard error.

abstract property Yscale

Observations scaling factor.

abstract property Yshift

Observations shift.

abstract property Ytrain

Training observations (only active dimensions, scaled).

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 dp_dx: ndarray[tuple[Any, ...], dtype[floating]]

Return ∂p/∂x.

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

Return ∂p/∂y.

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_database(dim: int) → None

Triggers the first database initialization.

Parameters:

dim (int) – Dimension of the fluid problem.

is_gp_model: bool

abstract property kernel_lengthscale

Kernel lengthscale(s).

abstract property kernel_variance

Kernel variance.

property last_fit_train_size

Size of the training database at the last fit.

max_steps: int

name: str = 'bulk'

noise: Tuple[float, float]

property objective

Optimization objective (negative marginal log likelihood)

abstract property obs_stddev

Observation noise standard deviation.

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]]

Return the pressure field.

prop: dict

rtol: float

save_state() → None

Log current GP hyperparameters and diagnostics.

similarity_check: bool

property solution

Return full solution field.

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

Return the bulk viscous stress field.

tol: str

property trusted: bool

Return True if model predictive variance is below tolerance.

update() → None

Compute and store bulk viscous stress using viscous model.

use_active_learning: bool