pde_trainingSelect_2var

Functions for selecting what training file specific solvePDE call directs too.

While available to user, not meant to be used.

`PINNtrainSelect_tx(pde_points, init_points, epochs, eqn, t_order, N_pde, N_iv, setup_boundaries, model, constraint, extra_ders, adapt, flag)`

Main selecting function for solvePDE_tx which determines what problem is being solved and directs information to the correct training file

Parameters:

pde_points (list) –

pde_points returned from defineCollocationPoints_tx()
init_points (list) –

inits returned from defineCollocationPoints_tx()
epochs (int) –

Number of epochs model gets trained for
eqn (string) –

Equation to be solved
t_order (int) –

Order of t in equation
N_pde (int) –

Number of randomly sampled collocation points along t and x which PINN uses in training.
N_iv (int) –

Number of randomly sampled collocation points along inital t which PINN uses in training.
setup_boundaries (boundary) –

boundary conditions set up from return of pde_Boundaries_2var call.
model (PINN) –

Model created from pde_ModelFuncs or input model
constraint (string) –

Constraint of inital conditions, "soft" or "hard"
extra_ders (list) –

Extra derivatives needed to be computed for user equation
flag (string) –

Internal flag for what type of equation is being solved

Returns:	`epoch_loss`( `list` ) – Total loss over training of model `iv_loss`( `list` ) – Inital value loss over training of model `bc_loss`( `list` ) – Boundary condition loss over training of model `pde_loss`( `list` ) – Differential equation loss over training of model `model`( `PINN` ) – Trained model to predict equation solution

Main selecting function for solvePDE_xy which determines what problem is being solved and directs information to the correct training file

Parameters:

pde_points (list) –

pde_points returned from defineCollocationPoints_xy()
epochs (int) –

Number of epochs model gets trained for
eqn (string) –

Equation to be solved
N_pde (int) –

Number of randomly sampled collocation points along x and y which PINN uses in training.
setup_boundaries (boundary) –

boundary conditions set up from return of pde_Boundaries_2var call.
model (PINN) –

Model created from pde_ModelFuncs or input model
constraint (string) –

Constraint of inital conditions, "soft" or "hard"
extra_ders (list) –

Extra derivatives needed to be computed for user equation
flag (string) –

Internal flag for what type of equation is being solved

Returns:	`epoch_loss`( `list` ) – Total loss over training of model `iv_loss`( `list` ) – Inital value loss over training of model `bc_loss`( `list` ) – Boundary condition loss over training of model `pde_loss`( `list` ) – Differential equation loss over training of model `model`( `PINN` ) – Trained model to predict equation solution

Main selecting function for solvePDE_DeepONet_tx which determines what problem is being solved and directs information to the correct training file

Parameters:

pde_points (list) –

pde_points returned from defineCollocationPoints_DON_tx()
init_points (list) –

inits returned from defineCollocationPoints_DON_tx()
epochs (int) –

Number of epochs model gets trained for
eqn (string) –

Equation to be solved
t_order (int) –

Order of t in equation
N_pde (int) –

Number of randomly sampled collocation points along t and x which DeepONet uses in training.
N_iv (int) –

Number of randomly sampled collocation points along inital t which DeepONet uses in training.
N_sensors (int) –

Number of sensors in which network learns over.
usensors (list) –

usensors returned from defineCollocationPoints_DON_tx()
sensor_range (list) –

Range in which sensors are sampled over.
setup_boundaries (boundary) –

boundary conditions set up from return of pde_Boundaries_2var call.
model (DeepONet) –

Model created from pde_ModelFuncs or input model
constraint (string) –

Constraint of inital conditions, "soft" or "hard"
extra_ders (list) –

Extra derivatives needed to be computed for user equation
flag (string) –

Internal flag for what type of equation is being solved

Returns:	`epoch_loss`( `list` ) – Total loss over training of model `iv_loss`( `list` ) – Inital value loss over training of model `bc_loss`( `list` ) – Boundary condition loss over training of model `pde_loss`( `list` ) – Differential equation loss over training of model `model`( `DeepONet` ) – Trained model to predict equation solution

Main selecting function for solvePDE_DeepONet_xy which determines what problem is being solved and directs information to the correct training file

Parameters:

pde_points (list) –

pde_points returned from defineCollocationPoints_DON_xy()
epochs (int) –

Number of epochs model gets trained for
eqn (string) –

Equation to be solved
N_pde (int) –

Number of randomly sampled collocation points along x and y which DeepONet uses in training.
N_bc (int) –

Number of randomly sampled collocation points along boundary which DeepONet uses in training.
N_sensors (int) –

Number of sensors in which network learns over.
usensors (list) –

usensors returned from defineCollocationPoints_DON_tx()
sensor_range (list) –

Range in which sensors are sampled over.
setup_boundaries (boundary) –

boundary conditions set up from return of pde_Boundaries_2var call.
model (DeepONet) –

Model created from pde_ModelFuncs or input model
constraint (string) –

Constraint of inital conditions, "soft" or "hard"
extra_ders (list) –

Extra derivatives needed to be computed for user equation
flag (string) –

Internal flag for what type of equation is being solved

Returns:	`epoch_loss`( `list` ) – Total loss over training of model `iv_loss`( `list` ) – Inital value loss over training of model `bc_loss`( `list` ) – Boundary condition loss over training of model `pde_loss`( `list` ) – Differential equation loss over training of model `model`( `DeepONet` ) – Trained model to predict equation solution