This notebook will get you started with DifferentialEquations.jl by introducing you to the functionality for solving ordinary differential equations (ODEs). The corresponding documentation page is the ODE tutorial. While some of the syntax may be different for other types of equations, the same general principles hold in each case. Our goal is to give a gentle and thorough introduction that highlights these principles in a way that will help you generalize what you have learned.

If you are new to the study of differential equations, it can be helpful to do a quick background read on the definition of ordinary differential equations. We define an ordinary differential equation as an equation which describes the way that a variable $u$ changes, that is

\[ u' = f(u,p,t) \]

where $p$ are the parameters of the model, $t$ is the time variable, and $f$ is the nonlinear model of how $u$ changes. The initial value problem also includes the information about the starting value:

\[ u(t_0) = u_0 \]

Together, if you know the starting value and you know how the value will change with time, then you know what the value will be at any time point in the future. This is the intuitive definition of a differential equation.

Our first model will be the canonical exponential growth model. This model says that the rate of change is proportional to the current value, and is this:

\[ u' = au \]

where we have a starting value $u(0)=u_0$. Let's say we put 1 dollar into Bitcoin which is increasing at a rate of $98\%$ per year. Then calling now $t=0$ and measuring time in years, our model is:

\[ u' = 0.98u \]

and $u(0) = 1.0$. We encode this into Julia by noticing that, in this setup, we match the general form when

f(u,p,t) = 0.98u

f (generic function with 1 method)

with $ u_0 = 1.0 $. If we want to solve this model on a time span from `t=0.0`

to `t=1.0`

, then we define an `ODEProblem`

by specifying this function `f`

, this initial condition `u0`

, and this time span as follows:

using DifferentialEquations f(u,p,t) = 0.98u u0 = 1.0 tspan = (0.0,1.0) prob = ODEProblem(f,u0,tspan)

ODEProblem with uType Float64 and tType Float64. In-place: false timespan: (0.0, 1.0) u0: 1.0

To solve our `ODEProblem`

we use the command `solve`

.

sol = solve(prob)

retcode: Success Interpolation: Automatic order switching interpolation t: 5-element Array{Float64,1}: 0.0 0.10042494449239292 0.35218603951893646 0.6934436028208104 1.0 u: 5-element Array{Float64,1}: 1.0 1.1034222047865465 1.4121908848175448 1.9730384275623003 2.664456142481452

and that's it: we have succesfully solved our first ODE!

Of course, the solution type is not interesting in and of itself. We want to understand the solution! The documentation page which explains in detail the functions for analyzing the solution is the Solution Handling page. Here we will describe some of the basics. You can plot the solution using the plot recipe provided by Plots.jl:

using Plots; gr() plot(sol)

From the picture we see that the solution is an exponential curve, which matches our intuition. As a plot recipe, we can annotate the result using any of the Plots.jl attributes. For example:

plot(sol,linewidth=5,title="Solution to the linear ODE with a thick line", xaxis="Time (t)",yaxis="u(t) (in μm)",label="My Thick Line!") # legend=false

Using the mutating `plot!`

command we can add other pieces to our plot. For this ODE we know that the true solution is $u(t) = u_0 exp(at)$, so let's add some of the true solution to our plot:

plot!(sol.t, t->1.0*exp(0.98t),lw=3,ls=:dash,label="True Solution!")

In the previous command I demonstrated `sol.t`

, which grabs the array of time points that the solution was saved at:

sol.t

5-element Array{Float64,1}: 0.0 0.10042494449239292 0.35218603951893646 0.6934436028208104 1.0

We can get the array of solution values using `sol.u`

:

sol.u

5-element Array{Float64,1}: 1.0 1.1034222047865465 1.4121908848175448 1.9730384275623003 2.664456142481452

`sol.u[i]`

is the value of the solution at time `sol.t[i]`

. We can compute arrays of functions of the solution values using standard comprehensions, like:

[t+u for (u,t) in tuples(sol)]

5-element Array{Float64,1}: 1.0 1.2038471492789395 1.7643769243364813 2.6664820303831105 3.664456142481452

However, one interesting feature is that, by default, the solution is a continuous function. If we check the print out again:

```
sol
```

retcode: Success Interpolation: Automatic order switching interpolation t: 5-element Array{Float64,1}: 0.0 0.10042494449239292 0.35218603951893646 0.6934436028208104 1.0 u: 5-element Array{Float64,1}: 1.0 1.1034222047865465 1.4121908848175448 1.9730384275623003 2.664456142481452

you see that it says that the solution has a order changing interpolation. The default algorithm automatically switches between methods in order to handle all types of problems. For non-stiff equations (like the one we are solving), it is a continuous function of 4th order accuracy. We can call the solution as a function of time `sol(t)`

. For example, to get the value at `t=0.45`

, we can use the command:

sol(0.45)

1.5542610480553116

DifferentialEquations.jl has a common set of solver controls among its algorithms which can be found at the Common Solver Options page. We will detail some of the most widely used options.

The most useful options are the tolerances `abstol`

and `reltol`

. These tell the internal adaptive time stepping engine how precise of a solution you want. Generally, `reltol`

is the relative accuracy while `abstol`

is the accuracy when `u`

is near zero. These tolerances are local tolerances and thus are not global guarantees. However, a good rule of thumb is that the total solution accuracy is 1-2 digits less than the relative tolerances. Thus for the defaults `abstol=1e-6`

and `reltol=1e-3`

, you can expect a global accuracy of about 1-2 digits. If we want to get around 6 digits of accuracy, we can use the commands:

sol = solve(prob,abstol=1e-8,reltol=1e-8)

retcode: Success Interpolation: Automatic order switching interpolation t: 9-element Array{Float64,1}: 0.0 0.04127492324135852 0.14679917846877366 0.28631546412766684 0.4381941361169628 0.6118924302028597 0.7985659100883337 0.9993516479536952 1.0 u: 9-element Array{Float64,1}: 1.0 1.0412786454705882 1.1547261252949712 1.3239095703537043 1.5363819257509728 1.8214895157178692 2.1871396448296223 2.662763824115295 2.664456241933517

Now we can see no visible difference against the true solution:

plot(sol) plot!(sol.t, t->1.0*exp(0.98t),lw=3,ls=:dash,label="True Solution!")

Notice that by decreasing the tolerance, the number of steps the solver had to take was `9`

instead of the previous `5`

. There is a trade off between accuracy and speed, and it is up to you to determine what is the right balance for your problem.

Another common option is to use `saveat`

to make the solver save at specific time points. For example, if we want the solution at an even grid of `t=0.1k`

for integers `k`

, we would use the command:

sol = solve(prob,saveat=0.1)

retcode: Success Interpolation: 1st order linear t: 11-element Array{Float64,1}: 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 u: 11-element Array{Float64,1}: 1.0 1.1029627851292922 1.2165269512238264 1.341783821227542 1.4799379510586077 1.6323162070541606 1.8003833264983586 1.9857565541588764 2.1902158127997704 2.4157257420844966 2.664456142481452

Notice that when `saveat`

is used the continuous output variables are no longer saved and thus `sol(t)`

, the interpolation, is only first order. We can save at an uneven grid of points by passing a collection of values to `saveat`

. For example:

sol = solve(prob,saveat=[0.2,0.7,0.9])

retcode: Success Interpolation: 1st order linear t: 3-element Array{Float64,1}: 0.2 0.7 0.9 u: 3-element Array{Float64,1}: 1.2165269512238264 1.9857565541588764 2.4157257420844966

If we need to reduce the amount of saving, we can also turn off the continuous output directly via `dense=false`

:

sol = solve(prob,dense=false)

retcode: Success Interpolation: 1st order linear t: 5-element Array{Float64,1}: 0.0 0.10042494449239292 0.35218603951893646 0.6934436028208104 1.0 u: 5-element Array{Float64,1}: 1.0 1.1034222047865465 1.4121908848175448 1.9730384275623003 2.664456142481452

and to turn off all intermediate saving we can use `save_everystep=false`

:

sol = solve(prob,save_everystep=false)

retcode: Success Interpolation: 1st order linear t: 2-element Array{Float64,1}: 0.0 1.0 u: 2-element Array{Float64,1}: 1.0 2.664456142481452

If we want to solve and only save the final value, we can even set `save_start=false`

.

sol = solve(prob,save_everystep=false,save_start = false)

retcode: Success Interpolation: 1st order linear t: 1-element Array{Float64,1}: 1.0 u: 1-element Array{Float64,1}: 2.664456142481452

Note that similarly on the other side there is `save_end=false`

.

More advanced saving behaviors, such as saving functionals of the solution, are handled via the `SavingCallback`

in the Callback Library which will be addressed later in the tutorial.

There is no best algorithm for numerically solving a differential equation. When you call `solve(prob)`

, DifferentialEquations.jl makes a guess at a good algorithm for your problem, given the properties that you ask for (the tolerances, the saving information, etc.). However, in many cases you may want more direct control. A later notebook will help introduce the various *algorithms* in DifferentialEquations.jl, but for now let's introduce the *syntax*.

The most crucial determining factor in choosing a numerical method is the stiffness of the model. Stiffness is roughly characterized by a Jacobian `f`

with large eigenvalues. That's quite mathematical, and we can think of it more intuitively: if you have big numbers in `f`

(like parameters of order `1e5`

), then it's probably stiff. Or, as the creator of the MATLAB ODE Suite, Lawrence Shampine, likes to define it, if the standard algorithms are slow, then it's stiff. We will go into more depth about diagnosing stiffness in a later tutorial, but for now note that if you believe your model may be stiff, you can hint this to the algorithm chooser via `alg_hints = [:stiff]`

.

sol = solve(prob,alg_hints=[:stiff])

retcode: Success Interpolation: specialized 3rd order "free" stiffness-aware interpolation t: 8-element Array{Float64,1}: 0.0 0.05653299582822294 0.17270731152826024 0.3164602871490142 0.5057500163821153 0.7292241858994543 0.9912975001018789 1.0 u: 8-element Array{Float64,1}: 1.0 1.0569657840332976 1.1844199383303913 1.3636037723365293 1.6415399686182572 2.043449143475479 2.6418256160577602 2.6644526430553808

Stiff algorithms have to solve implicit equations and linear systems at each step so they should only be used when required.

If we want to choose an algorithm directly, you can pass the algorithm type after the problem as `solve(prob,alg)`

. For example, let's solve this problem using the `Tsit5()`

algorithm, and just for show let's change the relative tolerance to `1e-6`

at the same time:

sol = solve(prob,Tsit5(),reltol=1e-6)

retcode: Success Interpolation: specialized 4th order "free" interpolation t: 10-element Array{Float64,1}: 0.0 0.028970819746309166 0.10049147151547619 0.19458908698515082 0.3071725081673423 0.43945421453622546 0.5883434923759523 0.7524873357619015 0.9293021330536031 1.0 u: 10-element Array{Float64,1}: 1.0 1.0287982807225062 1.1034941463604806 1.2100931078233779 1.351248605624241 1.538280340326815 1.7799346012651116 2.0905717422346277 2.4861021714470244 2.6644562434913373

Now let's move to a system of ODEs. The Lorenz equation is the famous "butterfly attractor" that spawned chaos theory. It is defined by the system of ODEs:

\[ \begin{align} \frac{dx}{dt} &= \sigma (y - x)\\ \frac{dy}{dt} &= x (\rho - z) -y\\ \frac{dz}{dt} &= xy - \beta z \end{align} \]

To define a system of differential equations in DifferentialEquations.jl, we define our `f`

as a vector function with a vector initial condition. Thus, for the vector `u = [x,y,z]'`

, we have the derivative function:

function lorenz!(du,u,p,t) σ,ρ,β = p du[1] = σ*(u[2]-u[1]) du[2] = u[1]*(ρ-u[3]) - u[2] du[3] = u[1]*u[2] - β*u[3] end

lorenz! (generic function with 1 method)

Notice here we used the in-place format which writes the output to the preallocated vector `du`

. For systems of equations the in-place format is faster. We use the initial condition $u_0 = [1.0,0.0,0.0]$ as follows:

u0 = [1.0,0.0,0.0]

3-element Array{Float64,1}: 1.0 0.0 0.0

Lastly, for this model we made use of the parameters `p`

. We need to set this value in the `ODEProblem`

as well. For our model we want to solve using the parameters $\sigma = 10$, $\rho = 28$, and $\beta = 8/3$, and thus we build the parameter collection:

p = (10,28,8/3) # we could also make this an array, or any other type!

(10, 28, 2.6666666666666665)

Now we generate the `ODEProblem`

type. In this case, since we have parameters, we add the parameter values to the end of the constructor call. Let's solve this on a time span of `t=0`

to `t=100`

:

tspan = (0.0,100.0) prob = ODEProblem(lorenz!,u0,tspan,p)

ODEProblem with uType Array{Float64,1} and tType Float64. In-place: true timespan: (0.0, 100.0) u0: [1.0, 0.0, 0.0]

Now, just as before, we solve the problem:

sol = solve(prob)

retcode: Success Interpolation: Automatic order switching interpolation t: 1294-element Array{Float64,1}: 0.0 3.5678604836301404e-5 0.0003924646531993154 0.0032624077544510573 0.009058075635317072 0.01695646895607931 0.0276899566248403 0.041856345938267966 0.06024040228733675 0.08368539694547242 ⋮ 99.39403070915297 99.47001147494375 99.54379656909015 99.614651558349 99.69093823148101 99.78733023233721 99.86114450046736 99.96115759510786 100.0 u: 1294-element Array{Array{Float64,1},1}: [1.0, 0.0, 0.0] [0.9996434557625105, 0.0009988049817849058, 1.781434788799208e-8] [0.9961045497425811, 0.010965399721242457, 2.146955365838907e-6] [0.9693591634199452, 0.08977060667778931, 0.0001438018342266937] [0.9242043615038835, 0.24228912482984957, 0.0010461623302512404] [0.8800455868998046, 0.43873645009348244, 0.0034242593451028745] [0.8483309877783048, 0.69156288756671, 0.008487623500490047] [0.8495036595681027, 1.0145425335433382, 0.01821208597613427] [0.9139069079152129, 1.4425597546855036, 0.03669381053327124] [1.0888636764765296, 2.052326153029042, 0.07402570506414284] ⋮ [12.999157033749652, 14.10699925404482, 31.74244844521858] [11.646131422021162, 7.2855792145502845, 35.365000488215486] [7.777555445486692, 2.5166095828739574, 32.030953593541675] [4.739741627223412, 1.5919220588229062, 27.249779003951755] [3.2351668945618774, 2.3121727966182695, 22.724936101772805] [3.310411964698304, 4.28106626744641, 18.435441144016366] [4.527117863517627, 6.895878639772805, 16.58544600757436] [8.043672261487556, 12.711555298531689, 18.12537420595938] [9.97537965430362, 15.143884806010783, 21.00643286956427]

The same solution handling features apply to this case. Thus `sol.t`

stores the time points and `sol.u`

is an array storing the solution at the corresponding time points.

However, there are a few extra features which are good to know when dealing with systems of equations. First of all, `sol`

also acts like an array. `sol[i]`

returns the solution at the `i`

th time point.

sol.t[10],sol[10]

(0.08368539694547242, [1.0888636764765296, 2.052326153029042, 0.07402570506 414284])

Additionally, the solution acts like a matrix where `sol[j,i]`

is the value of the `j`

th variable at time `i`

:

sol[2,10]

2.052326153029042

We can get a real matrix by performing a conversion:

A = Array(sol)

3×1294 Array{Float64,2}: 1.0 0.999643 0.996105 0.969359 … 4.52712 8.04367 9.97538 0.0 0.000998805 0.0109654 0.0897706 6.89588 12.7116 15.1439 0.0 1.78143e-8 2.14696e-6 0.000143802 16.5854 18.1254 21.0064

This is the same as sol, i.e. `sol[i,j] = A[i,j]`

, but now it's a true matrix. Plotting will by default show the time series for each variable:

plot(sol)

If we instead want to plot values against each other, we can use the `vars`

command. Let's plot variable `1`

against variable `2`

against variable `3`

:

plot(sol,vars=(1,2,3))