Surrogatize

 surrogatize(ed::ExperimentData, model; verbose = false, reg = 0.)

Construct a surrogate for a given dataset using a model.

The dataset must be of the type ExperimentData and the model is an instance of a surrogate model. These can be CTESN, ELM, AugmentedELM, etc.

The surrogatize function call returns a surrogate object that may be used to infer on incoming data. The API to infer using the trained surrogate is of the form

surr(u0, p, ts)

Here, u0 is a vector representing the initial conditions, p is a vector of parameters, and ts represents the time steps to evaluate the surrogate on. If ts is set to nothing, the function evaluation returns the value using the natural time steps the solver chooses.

Keyword Arguments:

• verbose: Boolean to enable printing of additional logging information about the progress in training the surrogate model
• reg: Sets the coefficient for L2 regularisation. 0. implies no regularisation is performed.

NOTE: The surrogates are only trained for autonomous dynamical models, and support for forced dynamical models is coming soon.

CTESN(
    RSIZE;
    weight_initializer,
    input_weight_initializer,
    initial_condition_initializer,
    alpha,
    tau,
    driver_sol,
    activation,
    solver,
    solver_kwargs
) -> CTESN

Constructs an instance of a CTESN object.

It operates using the formula:

((alpha - 1) * u) + σ.(W(u) + Win(sols(t)))

r is an integer to select the size of the reservoir used while training.

NOTE: default is to produce a linear projection CTESN.

Keyword Arguments

• weight_initializer: function that takes in 2 arguments and generates the W matrix.
• input_weight_initializer: function that takes in 2 arguments and generates the Win matrix.
• initial_condition_initializer: function that generates the fixed initial condition vector used by CTESN.
• alpha: value determining extent to which example solutions are embedded into the dynamics of the reservoir.
  • Value of 1.0 results in a strong memory of the driving solution/signal.
  • Value of 0.0 results in a weak memory of the driving solution/signal.
  • Also referred to as leakage rate.
• tau: effective time scale of dynamical system.
• driver_sol: NamedTuple of arguments used to generate the driver solution to train the CTESN.
  • All the keys as in the default configuration must be present.
  • The keys are (:lb, :ub, :count, :order, :idxs)
• activation: nonlinearity shown as σ in the formula.
  • Note that it operates in an elementwise fashion.
• solver, solver_kwargs: solver used to evolve the CTESN along with any keyword arguments.

ELM([tanh ], in, hidden_size;
    initW = randn,
    initb = randn,
    radial_function = linearRadial(),
    solver = Rosenbrock23(),
    solver_kwargs = (;))

Constructs an instance of the Extreme Learining Machine (ELM) where in is an integer representing the input dimension and hidden_size is an integer representing the size of the hidden features.

Optionally, one can provide the activation function as the first argument. Defaults to tanh.

By default, the ELM model only operates on states and discards the controls and parameters. AugmentedELM can be used for training on the controls and parameters in addition to the states.

Keyword Arguments

• initW: function used to generate the weights
• initb: function used to generate the bias
• solver: represents the solver chosen to evolve the trained surrogate with
• radial_function: the radial distance metric used to generate the RadialBasis hypernetwork
• solver_kwargs: keyword arguments to be set alongside the chosen solver

AugmentedELM([tanh, ], in, hidden_size;
             initW = randn,
             initb = randn,
             solver = Rosenbrock23(),
             solver_kwargs = (;))

Constructs an instance of the Extreme Learining Machine (ELM) where in is an integer representing in the input dimension and hidden_size is an integer representing the size of the hidden dimension.

Optionally, one can provide the activation function as the first argument. Defults to tanh.

It differs from the standard ELM by learning on the states as well as the parameters of a simulation.

NOTE: Since the AugmentedELM doesn't require a RadialBasis hypernetwork, it doesn't accept the radial_function keyword argument.

Keyword Arguments

• initW: function used to generate the weights
• initb: function used to generate the bias
• solver: represents the solver chosen to evolve the trained surrogate with
• solver_kwargs: keyword arguments to be set alongside the chosen solver