API
Optimizer

Module PINNacle.src.optimizer

Sub-modules

  • PINNacle.src.optimizer.adam_lbfgs
  • PINNacle.src.optimizer.lr_adaptor
  • PINNacle.src.optimizer.multiadam
  • PINNacle.src.optimizer.ntk

Classes

Adam_LBFGS(params, switch_epoch=10000, adam_param={'lr': 0.001, 'betas': (0.9, 0.999)}, lbfgs_param={'lr': 1, 'max_iter': 20}) : Base class for all optimizers.

.. warning::
    Parameters need to be specified as collections that have a deterministic
    ordering that is consistent between runs. Examples of objects that don't
    satisfy those properties are sets and iterators over values of dictionaries.

Args:
    params (iterable): an iterable of :class:`torch.Tensor` s or
        :class:`dict` s. Specifies what Tensors should be optimized.
    defaults: (dict): a dict containing default values of optimization
        options (used when a parameter group doesn't specify them).

### Ancestors (in MRO)

* torch.optim.optimizer.Optimizer

### Methods

`step(self, closure=None)`
:   Performs a single optimization step (parameter update).

    Args:
        closure (Callable): A closure that reevaluates the model and
            returns the loss. Optional for most optimizers.

    .. note::
        Unless otherwise specified, this function should not modify the
        ``.grad`` field of the parameters.

LR_Adaptor(optimizer, loss_weight, num_pde, alpha=0.1, mode='max') : PINN callback for learning rate annealing algorithm of physics-informed neural networks.

loss_weight - initial loss weights
num_pde - the number of the PDEs (boundary conditions excluded)
alpha - parameter of moving average
mode - "max" (PINN-LA), "mean" (PINN-LA-2)

### Ancestors (in MRO)

* torch.optim.optimizer.Optimizer

### Methods

`step(self, closure)`
:

LR_Adaptor_NTK(optimizer, loss_weight, pde) : PINN callback for learning rate annealing algorithm of physics-informed neural networks.

loss_weight - initial loss weights
num_pde - the number of the PDEs (boundary conditions excluded)

### Ancestors (in MRO)

* torch.optim.optimizer.Optimizer

### Methods

`step(self, closure)`
:

MultiAdam(params, lr=0.001, betas=(0.99, 0.99), eps=1e-08, weight_decay=0, amsgrad=False, maximize=False, loss_group_idx=None, group_weights=None, agg_momentum=False, agg_betas=None, *, param_scheduler=None) : Base class for all optimizers.

.. warning::
    Parameters need to be specified as collections that have a deterministic
    ordering that is consistent between runs. Examples of objects that don't
    satisfy those properties are sets and iterators over values of dictionaries.

Args:
    params (iterable): an iterable of :class:`torch.Tensor` s or
        :class:`dict` s. Specifies what Tensors should be optimized.
    defaults: (dict): a dict containing default values of optimization
        options (used when a parameter group doesn't specify them).

### Ancestors (in MRO)

* torch.optim.optimizer.Optimizer

### Methods

`init_states(self)`
:

`step(self, closure)`
:   Performs a single optimization step.

    Args:
        closure (callable, optional): A closure that reevaluates the model
            and returns the loss.
LaafAdam Lbfgs