Here’s a very simple example:

```
using DifferentialEquations
function ode!(du,u,p,t)
du[1] =1- u[1]
end
problem=ODEProblem(ode!,[0.0],[0.0,5.0])
sol=solve(problem,Tsit5())
```

If you examine the `sol`

object after running that code, you see this:

```
julia> sol
retcode: Success
Interpolation: specialized 4th order "free" interpolation
t: 15-element Array{Float64,1}:
0.0
9.999999999999999e-5
0.0010999999999999998
0.011099999999999997
0.07674209860034185
0.2256220209761885
0.4455760494689467
0.7272505875899613
1.08996450301086
1.5331654123549805
2.0697241857623103
2.705750702626476
3.4562447902280993
4.337840756338852
5.0
u: 15-element Array{Array{Float64,1},1}:
[0.0]
[9.999500016666247e-5]
[0.001099395221772342]
[0.011038622307372232]
[0.07387132730531631]
[0.20198031990033405]
[0.35954475580746376]
[0.5167641828239731]
[0.6637714219615871]
[0.7841482550875905]
[0.8737784281094817]
[0.9331779992336123]
[0.9684489722571047]
[0.9869310694285471]
[0.9932596937930706]
```

It only stored 15 points over the 5 seconds of simulation. However, you can get the value at any time by executing something like `sol(1.0)`

(note I’m using parentheses, not brackets) and it will interpolate and give an accurate answer.

Or you can do this:

```
julia> sol(0.0:0.5:5.0)
t: 0.0:0.5:5.0
u: 11-element Array{Array{Float64,1},1}:
[0.0]
[0.39346948341153787]
[0.6321206591004334]
[0.7768694671005957]
[0.8646641097009187]
[0.9179166359464174]
[0.9502181305661904]
[0.9698001352429271]
[0.9816881279747315]
[0.9888878525731617]
[0.9932596937930706]
```

to get interpolation of results on a regular time basis.

Docs are here: https://docs.sciml.ai/stable/basics/solution/#Interpolations-1