Symbolic ODE Solving

While not all ODEs have an analytical solution, symbolic ODE solving is provided by Symbolics.jl for subsets of cases for which known analytical solutions can be obtained. These expressions can then be merged with other techniques in order to accelerate code or gain a deeper understanding of real-world systems.

Merging Symbolic ODEs with Numerical Methods: ModelingToolkit.jl

If you are looking to merge Symbolics.jl manipulations with numerical solvers such as DifferentialEquations.jl, then we highly recommend checking out the ModelingToolkit.jl system. It represents systems of differentible-algebraic equations (DAEs, and extension to ODEs) for which a sophisticated symbolic analysis pipeline is used to generate highly efficient code. This mtkcompile pipeline makes use of all tricks from Symbolics.jl and many that are more specific to numerical code generation, such as Pantelides index reduction and tearing of nonlinear systems, and it will analytically solve subsets of the ODE system as finds possible. Thus if you are attempting to use Symbolics to pre-solve some parts of an ODE analytically, we recommend allowing ModelingToolkit.jl to do this optimization.

Note that if ModelingToolkit is able to analytically solve the equation, it will give an ODEProblem where prob.u0 === nothing, and then running solve on the ODEProblem will give a numerical ODESolution object that on-demand uses the analytical solution to generate any plots or other artifacts. The analytical solution can be investigated symbolically using observed(sys).

Symbolically Solving ODEs

Note

This area is currently under heavy development. More solvers will be available in the near future.

Symbolics.LinearODE — Type

Represents a linear ordinary differential equation of the form:

dⁿx/dtⁿ + pₙ(t)(dⁿ⁻¹x/dtⁿ⁻¹) + ... + p₂(t)(dx/dt) + p₁(t)x = q(t)

Fields

x: dependent variable
t: independent variable
p: coefficient functions of t ordered in increasing order (p₁, p₂, ...)
q: right hand side function of t, without any x

Examples

julia> using Symbolics

julia> @variables x, t
2-element Vector{Num}:
 x
 t

julia> eq = LinearODE(x, t, [1, 2, 3], 3exp(4t))
(Dt^3)x + (3)(Dt^2)x + (2)(Dt^1)x + (1)(Dt^0)x ~ 3exp(4t)

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Symbolics.symbolic_solve_ode — Function

symbolic_solve_ode(eq::LinearODE)

Symbolically solve a linear ordinary differential equation

Arguments

eq: a LinearODE to solve

Returns

Symbolic solution to the ODE

Supported Methods

first-order integrating factor
constant coefficient homogeneous solutions (can handle repeated and complex characteristic roots)
exponential and resonant response formula particular solutions (for any linear combination of exp, sin, cos, or exp times sin or cos (e.g. e^2t * cos(-t) + e^-3t + sin(5t)))
method of undetermined coefficients particular solutions
linear combinations of above particular solutions

Examples

julia> using Symbolics; import Nemo, SymPy

julia> @variables x, t
2-element Vector{Num}:
 x
 t

# Integrating Factor (note that SymPy is required for integration)
julia> symbolic_solve_ode(LinearODE(x, t, [5/t], 7t))
(C₁ + t^7) / (t^5)

# Constant Coefficients and RRF (note that Nemo is required to find characteristic roots)
julia> symbolic_solve_ode(LinearODE(x, t, [9, -6], 4exp(3t)))
C₁*exp(3t) + C₂*t*exp(3t) + (2//1)*(t^2)*exp(3t)

julia> symbolic_solve_ode(LinearODE(x, t, [6, 5], 2exp(-t)*cos(t)))
C₁*exp(-2t) + C₂*exp(-3t) + (1//5)*cos(t)*exp(-t) + (3//5)*exp(-t)*sin(t)

# Method of Undetermined Coefficients
julia> symbolic_solve_ode(LinearODE(x, t, [-3, 2], 2t - 5))
(11//9) - (2//3)*t + C₁*exp(t) + C₂*exp(-3t)

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symbolic_solve_ode(expr::Equation, x, t)

Symbolically solve an ODE

Arguments

expr: a symbolic ODE
x: dependent variable
t: independent variable

Supported Methods

all methods of solving linear ODEs mentioned for symbolic_solve_ode(eq::LinearODE)
Clairaut's equation
Bernoulli equations

Examples

julia> using Symbolics; import Nemo

julia> @variables x, t
2-element Vector{Num}:
 x
 t

julia> Dt = Differential(t)
Differential(t)

# LinearODE (via constant coefficients and RRF)
julia> symbolic_solve_ode(9t*x - 6*Dt(x) ~ 4exp(3t), x, t)
C₁*exp(3t) + C₂*t*exp(3t) + (2//1)*(t^2)*exp(3t)

# Clairaut's equation
julia> symbolic_solve_ode(x ~ Dt(x)*t - ((Dt(x))^3), x, t)
C₁*t - (C₁^3)

# Bernoulli equations
julia> symbolic_solve_ode(Dt(x) + (4//t)*x ~ t^3 * x^2, x, t)
1 / (C₁*(t^4) - (t^4)*log(t))

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Continuous Dynamical Systems

Symbolics.solve_linear_system — Function

solve_linear_system(A::Matrix{<:Number}, x0::Vector{<:Number}, t::Num)

Solve linear continuous dynamical system of differential equations of the form Ax = x' with initial condition x0

Arguments

A: matrix of coefficients
x0: initial conditions vector
t: independent variable

Returns

vector of symbolic solutions

Examples

!!! note uses method symbolic_solve, so packages Nemo and Groebner are often required

julia> @variables t
1-element Vector{Num}:
 t

julia> solve_linear_system([1 0; 0 -1], [1, -1], t) # requires Nemo
2-element Vector{Num}:
   exp(t)
 -exp(-t)

julia> solve_linear_system([-3 4; -2 3], [7, 2], t) # requires Groebner
2-element Vector{Num}:
 (10//1)*exp(-t) - (3//1)*exp(t)
  (5//1)*exp(-t) - (3//1)*exp(t)

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SymPy

Symbolics.sympy_ode_solve — Function

sympy_ode_solve(expr, func, var)

Solves ODE expr = 0 for function func w.r.t. var using SymPy.

Arguments

expr: Symbolics expression representing ODE (set to 0).
func: Symbolics function (e.g., f(x)).
var: Independent Symbolics variable.

Returns

Symbolics solution(s).

Example

@variables x
@syms f(x)
expr = Symbolics.Derivative(f, x) - 2*f
sol = sympy_ode_solve(expr, f, x)  # Returns C1*exp(2*x)

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