torchphysics.problem.domains.functionsets package

Function sets can be used to sample functions, e.g. in DeepONet.

Submodules

torchphysics.problem.domains.functionsets.functionset module

class torchphysics.problem.domains.functionsets.functionset.CustomFunctionSet(function_space, parameter_sampler, custom_fn)[source]

Bases: FunctionSet

FunctionSet for an arbitrary function.

Parameters:

function_space (torchphysics.spaces.FunctionSpace) – The space of which this set of functions belongs to. The inputs and outputs of this FunctionSet are defined by the corresponding values inside the function space.
parameter_sampler (torchphysics.samplers.PointSampler) –
A sampler that provides additional parameters that can be used to create different kinds of functions. E.g. our FunctionSet consists of Functions like k*x, x is the input variable and k is given through the sampler.

During each training iteration will call the parameter_sampler to sample new parameters. For each parameter a function will be created and the input batch of functions will be of the same length as the sampled parameters.
custom_fn (callable) – A function that describes the FunctionSet. The input of the functions can include the variables of the function_space.input_space and the parameters from the parameter_sampler.

class torchphysics.problem.domains.functionsets.functionset.FunctionSet(function_space, parameter_sampler)[source]

Bases: object

A set of functions that can supply samples from a function space.

Parameters:

function_space (torchphysics.spaces.FunctionSpace) – The space of which this set of functions belongs to. The inputs and outputs of this FunctionSet are defined by the corresponding values inside the function space.
parameter_sampler (torchphysics.samplers.PointSampler) –
A sampler that provides additional parameters that can be used to create different kinds of functions. E.g. our FunctionSet consists of Functions like k*x, x is the input variable and k is given through the sampler.

During each training iteration will call the parameter_sampler to sample new parameters. For each parameter a function will be created and the input batch of functions will be of the same length as the sampled parameters.

__add__(other)[source]

Combines two function sets.

Notes

When parameters are sampled, will sample them from both sets. Creates a batch of functions consisting of the batch of each set. (Length of the batches will be added)

__len__()[source]: Returns the amount of functions sampled in a single call to sample_params.

create_function_batch(points)[source]

Evaluates the underlying function object to create a batch of discrete function samples.

Parameters:: points (torchphysics.spaces.Points) – The input points, where we want to evaluate a set of functions.
Returns:: The batch of discrete function samples. The underlying tensor is of the shape: [len(self), len(points), self.function_space.output_space.dim]
Return type:: torchphysics.spaces.Points

sample_params(device='cpu')[source]

Samples parameters of the function space.

Parameters:: device (str, optional) – The device, where the parameters should be created. Default is ‘cpu’.

Notes

We save the sampled parameters internally, so that we can use them multiple times. Since given a parameter we still have a continuous representation of the underlying function types. When the functions should be evaluated at some input points, we just have to create the meshgrid of parameters and points.

class torchphysics.problem.domains.functionsets.functionset.FunctionSetCollection(function_sets)[source]

Bases: FunctionSet

Collection of multiple FunctionSets. Used for the additions of different FunctionSets.

Parameters:: function_sets (list, tuple) – A list/tuple of FunctionSets.

__add__(other)[source]

Combines two function sets.

Notes

When parameters are sampled, will sample them from both sets. Creates a batch of functions consisting of the batch of each set. (Length of the batches will be added)

__len__()[source]: Returns the amount of functions sampled in a single call to sample_params.

create_function_batch(points)[source]

Evaluates the underlying function object to create a batch of discrete function samples.

Parameters:: points (torchphysics.spaces.Points) – The input points, where we want to evaluate a set of functions.
Returns:: The batch of discrete function samples. The underlying tensor is of the shape: [len(self), len(points), self.function_space.output_space.dim]
Return type:: torchphysics.spaces.Points

sample_params(device='cpu')[source]

Samples parameters of the function space.

Parameters:: device (str, optional) – The device, where the parameters should be created. Default is ‘cpu’.

Notes

We save the sampled parameters internally, so that we can use them multiple times. Since given a parameter we still have a continuous representation of the underlying function types. When the functions should be evaluated at some input points, we just have to create the meshgrid of parameters and points.