ODESystem

struct ODESystem <: ModelingToolkit.AbstractODESystem

A system of ordinary differential equations.

Fields

• tag: tag: a tag for the system. If two systems have the same tag, then they are structurally identical.

• eqs: The ODEs defining the system.

• iv: Independent variable.

• states: Dependent (state) variables. Must not contain the independent variable.

  N.B.: If tornmatching !== nothing, this includes all variables. Actual ODE states are determined by the SelectedState() entries in `tornmatching`.

• ps: Parameter variables. Must not contain the independent variable.

• tspan: Time span.

• var_to_name: Array variables.

• ctrls: Control parameters (some subset of ps).

• observed: Observed states.

• tgrad: Time-derivative matrix. Note: this field will not be defined until calculate_tgrad is called on the system.

• jac: Jacobian matrix. Note: this field will not be defined until calculate_jacobian is called on the system.

• ctrl_jac: Control Jacobian matrix. Note: this field will not be defined until calculate_control_jacobian is called on the system.

• Wfact: Wfact matrix. Note: this field will not be defined until generate_factorized_W is called on the system.

• Wfact_t: Wfact_t matrix. Note: this field will not be defined until generate_factorized_W is called on the system.

• name: Name: the name of the system

• systems: systems: The internal systems. These are required to have unique names.

• defaults: defaults: The default values to use when initial conditions and/or parameters are not supplied in ODEProblem.

• torn_matching: torn_matching: Tearing result specifying how to solve the system.

• connector_type: connector_type: type of the system

• preface: preface: inject assignment statements before the evaluation of the RHS function.

• continuous_events: continuous_events: A Vector{SymbolicContinuousCallback} that model events. The integrator will use root finding to guarantee that it steps at each zero crossing.

• discrete_events: discrete_events: A Vector{SymbolicDiscreteCallback} that models events. Symbolic analog to SciMLBase.DiscreteCallback that executes an affect when a given condition is true at the end of an integration step.

• metadata: metadata: metadata for the system, to be used by downstream packages.

• gui_metadata: gui_metadata: metadata for MTK GUI.

• tearing_state: tearing_state: cache for intermediate tearing state

• substitutions: substitutions: substitutions generated by tearing.

• complete: complete: if a model sys is complete, then sys.x no longer performs namespacing.

• discrete_subsystems: discrete_subsystems: a list of discrete subsystems.

• unknown_states: unknown_states: a list of actual states needed to be solved by solvers. Only used for ODAEProblem.

Example

using ModelingToolkit

@parameters σ ρ β
@variables t x(t) y(t) z(t)
D = Differential(t)

eqs = [D(x) ~ σ*(y-x),
       D(y) ~ x*(ρ-z)-y,
       D(z) ~ x*y - β*z]

@named de = ODESystem(eqs,t,[x,y,z],[σ,ρ,β],tspan=(0, 1000.0))

ode_order_lowering(sys::ODESystem) -> Any

Takes a Nth order ODESystem and returns a new ODESystem written in first order form by defining new variables which represent the N-1 derivatives.

dae_index_lowering(sys::ODESystem; kwargs...) -> ODESystem

Perform the Pantelides algorithm to transform a higher index DAE to an index 1 DAE. kwargs are forwarded to pantelides!. End users are encouraged to call structural_simplify instead, which calls this function internally.

liouville_transform(sys::ModelingToolkit.AbstractODESystem)

Generates the Liouville transformed set of ODEs, which is the original ODE system with a new variable trJ appended, corresponding to the -tr(Jacobian). This variable is used for properties like uncertainty propagation from a given initial distribution density.

For example, if $u'=p*u$ and p follows a probability distribution $f(p)$, then the probability density of a future value with a given choice of $p$ is computed by setting the initial trJ = f(p), and the final value of trJ is the probability of $u(t)$.

Example:

using ModelingToolkit, OrdinaryDiffEq, Test

@parameters t α β γ δ
@variables x(t) y(t)
D = Differential(t)

eqs = [D(x) ~ α*x - β*x*y,
       D(y) ~ -δ*y + γ*x*y]

sys = ODESystem(eqs)
sys2 = liouville_transform(sys)
@variables trJ

u0 = [x => 1.0,
      y => 1.0,
      trJ => 1.0]

prob = ODEProblem(sys2,u0,tspan,p)
sol = solve(prob,Tsit5())

Where sol[3,:] is the evolution of trJ over time.

Sources:

Probabilistic Robustness Analysis of F-16 Controller Performance: An Optimal Transport Approach

Abhishek Halder, Kooktae Lee, and Raktim Bhattacharya https://abhishekhalder.bitbucket.io/F16ACC2013Final.pdf

DiffEqBase.ODEFunction{iip}(sys::AbstractODESystem, dvs = states(sys),
                            ps = parameters(sys);
                            version = nothing, tgrad = false,
                            jac = false,
                            sparse = false,
                            kwargs...) where {iip}

Create an ODEFunction from the ODESystem. The arguments dvs and ps are used to set the order of the dependent variable and parameter vectors, respectively.

DiffEqBase.ODEProblem{iip}(sys::AbstractODESystem, u0map, tspan,
                           parammap = DiffEqBase.NullParameters();
                           version = nothing, tgrad = false,
                           jac = false,
                           checkbounds = false, sparse = false,
                           simplify = false,
                           linenumbers = true, parallel = SerialForm(),
                           kwargs...) where {iip}

Generates an ODEProblem from an ODESystem and allows for automatically symbolically calculating numerical enhancements.

SciMLBase.SteadyStateProblem(sys::AbstractODESystem, u0map,
                             parammap = DiffEqBase.NullParameters();
                             version = nothing, tgrad = false,
                             jac = false,
                             checkbounds = false, sparse = false,
                             linenumbers = true, parallel = SerialForm(),
                             kwargs...) where {iip}

Generates an SteadyStateProblem from an ODESystem and allows for automatically symbolically calculating numerical enhancements.

ODEFunctionExpr{iip}(sys::AbstractODESystem, dvs = states(sys),
                     ps = parameters(sys);
                     version = nothing, tgrad = false,
                     jac = false,
                     sparse = false,
                     kwargs...) where {iip}

Create a Julia expression for an ODEFunction from the ODESystem. The arguments dvs and ps are used to set the order of the dependent variable and parameter vectors, respectively.

DAEFunctionExpr{iip}(sys::AbstractODESystem, dvs = states(sys),
                     ps = parameters(sys);
                     version = nothing, tgrad = false,
                     jac = false,
                     sparse = false,
                     kwargs...) where {iip}

Create a Julia expression for an ODEFunction from the ODESystem. The arguments dvs and ps are used to set the order of the dependent variable and parameter vectors, respectively.

SciMLBase.SteadyStateProblemExpr(sys::AbstractODESystem, u0map,
                                 parammap = DiffEqBase.NullParameters();
                                 version = nothing, tgrad = false,
                                 jac = false,
                                 checkbounds = false, sparse = false,
                                 skipzeros = true, fillzeros = true,
                                 linenumbers = true, parallel = SerialForm(),
                                 kwargs...) where {iip}

Generates a Julia expression for building a SteadyStateProblem from an ODESystem and allows for automatically symbolically calculating numerical enhancements.