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README.md

ParameterizedFunctions.jl

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ParameterizedFunctions.jl is a component of the SciML ecosystem which allows for easily defining parameterized ODE models in a simple syntax.

Basic Usage

ODE Macros

A helper macro is provided to make it easier to define a ParameterizedFunction, and it will symbolically compute a bunch of extra functions to make the differential equation solvers run faster. For example, to define the previous LotkaVolterra, you can use the following command:

f = @ode_def LotkaVolterra begin
  dx = a*x - b*x*y
  dy = -c*y + d*x*y
end a b c d

or you can define it anonymously:

f = @ode_def begin
  dx = a*x - b*x*y
  dy = -c*y + d*x*y
end a b c d

The macro also defines the Jacobian f'. This is defined as an in-place Jacobian f(Val{:jac},t,u,J). This is calculated using SymEngine.jl automatically, so it's no effort on your part. The symbolic inverse of the Jacobian is also computed, and an in-place function for this is available as well as f(Val{:invjac},t,u,iJ). If the Jacobians cannot be computed, a warning is thrown and only the function itself is usable. The functions jac_exists(f) and invjac_exists(f) can be used to see whether the Jacobian and the function for its inverse exist.

Extra Options

In most cases the @ode_def macro should be sufficient. This is because by default the macro will simply calculate each function symbolically, and if it can't it will simply throw a warning and move on. However, in extreme cases the symbolic calculations may take a long time, in which case it is necessary to turn them off. To do this, use the ode_def_opts function. The @ode_def macro simply defines the specifiable options:

opts = Dict{Symbol,Bool}(
      :build_tgrad => true,
      :build_jac => true,
      :build_expjac => false,
      :build_invjac => true,
      :build_invW => true,
      :build_invW_t => true,
      :build_hes => false,
      :build_invhes => false,
      :build_dpfuncs => true)

and calls the function ode_def_opts(name::Symbol,opts,ex::Expr,params). Note that params is an iterator holding expressions for the parameters.

In addition, one can also use their own function inside of the macro. For example:

f(x,y,d) = erf(x*y/d)
NJ = @ode_def FuncTest begin
  dx = a*x - b*x*y
  dy = -c*y + f(x,y,d)
end a b c d

will do fine. The symbolic derivatives will not work unless you define a derivative for f.

Extra Macros

Instead of using ode_def_opts directly, one can use one of the following macros to be more specific about what to not calculate. In increasing order of calculations:

@ode_def_bare
@ode_def
@ode_def_all

Extra Functions

Jacobian Function

The Jacobian overload is provided by overloading in the following manner:

function (p::LotkaVolterra)(::Type{Val{:jac}},t,u,J)
  J[1,1] = p.a - p.b * u[2]
  J[1,2] = -(p.b) * u[1]
  J[2,1] = 1 * u[2]
  J[2,2] = -3 + u[1]
  nothing
end

Inverse Jacobian

The Inverse Jacobian overload is provided by overloading in the following manner:

function (p::LotkaVolterra)(::Type{Val{:invjac}},t,u,J)
  J[1,1] = (1 - (p.b * u[1] * u[2]) / ((p.a - p.b * u[2]) * (-3 + u[1] + (p.b * u[1] * u[2]) / (p.a - p.b * u[2])))) / (p.a - p.b * u[2])
  J[1,2] = (p.b * u[1]) / ((p.a - p.b * u[2]) * (-3 + u[1] + (p.b * u[1] * u[2]) / (p.a - p.b * u[2])))
  J[2,1] = -(u[2]) / ((p.a - p.b * u[2]) * (-3 + u[1] + (p.b * u[1] * u[2]) / (p.a - p.b * u[2])))
  J[2,2] = (-3 + u[1] + (p.b * u[1] * u[2]) / (p.a - p.b * u[2])) ^ -1
  nothing
end

Parameter Jacobian

For solvers which need parameters derivatives, specifying the functions can increase performance. For our example, we allow the solvers to use the explicit derivatives in the parameters by:

function (p::LotkaVolterra)(::Type{Val{:paramjac}},J,u,p,t)
    J[1, 1] = u[1] * 1
    J[1, 2] = -(u[1]) * u[2]
    J[1, 3] = 0 * 1
    J[1, 4] = 0 * 1
    J[2, 1] = 0 * 1
    J[2, 2] = 0 * 1
    J[2, 3] = -(u[2])
    J[2, 4] = u[1] * u[2]
    nothing
end

About

A simple domain-specific language (DSL) for defining differential equations for use in scientific machine learning (SciML) and other applications

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