Basis

DataDrivenDiffEq.BasisType
struct Basis{IMPL, CTRLS} <: DataDrivenDiffEq.AbstractBasis

A basis over the states with parameters, independent variable, and possible exogenous controls. It extends an AbstractSystem as defined in ModelingToolkit.jl. f can either be a Julia function which is able to use ModelingToolkit variables or a vector of eqs. It can be called with the typical SciML signature, meaning out of place with f(u,p,t) or in place with f(du, u, p, t). If control inputs are present, it is assumed that no control corresponds to zero for all inputs. The corresponding function calls are f(u,p,t,inputs) and f(du,u,p,t,inputs) and need to be specified fully.

The optional implicits declare implicit variables in the Basis, meaning variables representing the (measured) target of the system. Right now, only supported with the use of ImplicitOptimizers.

If linear_independent is set to true, a linear independent basis is created from all atom functions in f.

If simplify_eqs is set to true, simplify is called on f.

Additional keyword arguments include name, which can be used to name the basis, and observed for defining observables.

Fields

  • eqs: The equations of the basis

  • unknowns: Dependent (state) variables

  • ctrls: Control variables

  • ps: Parameters

  • observed: Observed

  • iv: Independent variable

  • implicit: Implicit variables of the basis

  • f: Internal function representation of the basis

  • name: Name of the basis

  • systems: Internal systems

Example

using ModelingToolkit
using DataDrivenDiffEq

@parameters w[1:2] t
@variables u[1:2](t)

Ψ = Basis([u; sin.(w.*u)], u, parameters = p, iv = t)

Note

The keyword argument eval_expression controls the function creation behavior. eval_expression=true means that eval is used, so normal world-age behavior applies (i.e. the functions cannot be called from the function that generates them). If eval_expression=false, then construction via GeneralizedGenerated.jl is utilized to allow for same world-age evaluation. However, this can cause Julia to segfault on sufficiently large basis functions. By default eval_expression=false.

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API

Next to ModelingToolkits API for AbstractSystems, a Basis can be called with the following methods:

DataDrivenDiffEq.dynamicsFunction
dynamics(b)
Returns the internal function representing the dynamics of the `Basis`. This can be called either inplace or out-of-place
with the typical SciML signature `f(u,p,t)` or `f(du,u,p,t)`. If control variables are defined, the function can also be called
by `f(u,p,t,control)` or `f(du,u,p,t,control)` and assumes `control .= 0` if no control is given.
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DataDrivenDiffEq.jacobianFunction
jacobian(x)
jacobian(x, eval_expression)

Returns a function representing the Jacobian matrix / gradient of the Basis with respect to the states as a function with the common signature f(u,p,t) for out of place and f(du, u, p, t) for in place computation. If control variables are defined, the function can also be called by f(u,p,t,control) or f(du,u,p,t,control) and assumes control .= 0 if no control is given.

If the Jacobian with respect to other variables is needed, it can be passed via a second argument.

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DataDrivenDiffEq.get_parameter_valuesFunction
get_parameter_values(x)

Return the default values for the given Basis. If no default value is stored, returns zero(T) where T is the symtype of the parameter.

Note

This extends getmetadata in a way that all parameters have a numeric value.

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DataDrivenDiffEq.get_parameter_mapFunction
get_parameter_map(x)

Return the default values as a vector of pairs for the given Basis. If no default value is stored, returns zero(T) where T is the symtype of the parameter.

Note

This extends getmetadata in a way that all parameters have a numeric value.

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Generators

DataDrivenDiffEq.monomial_basisFunction
monomial_basis(x)
monomial_basis(x, degree)

Constructs an array containing monomial basis in the variables x up to degree c of the form [x₁, x₁^2, ... , x₁^c, x₂, x₂^2, ...].

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DataDrivenDiffEq.polynomial_basisFunction
polynomial_basis(x)
polynomial_basis(x, degree)

Constructs an array containing a polynomial basis in the variables x up to degree c of the form [x₁, x₂, x₃, ..., x₁^1 * x₂^(c-1)]. Mixed terms are included.

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DataDrivenDiffEq.sin_basisFunction
sin_basis(x, coefficients)

Constructs an array containing a Sine basis in the variables x with coefficients c. If c is an Int returns all coefficients from 1 to c.

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DataDrivenDiffEq.cos_basisFunction
cos_basis(x, coefficients)

Constructs an array containing a Cosine basis in the variables x with coefficients c. If c is an Int returns all coefficients from 1 to c.

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DataDrivenDiffEq.fourier_basisFunction
fourier_basis(x, coefficients)

Constructs an array containing a Fourier basis in the variables x with (integer) coefficients c. If c is an Int returns all coefficients from 1 to c.

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DataDrivenDiffEq.chebyshev_basisFunction
chebyshev_basis(x, coefficients)

Constructs an array containing a Chebyshev basis in the variables x with coefficients c. If c is an Int returns all coefficients from 1 to c.

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