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Jun 9, 2023
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docs: performative -> performant #185

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4 changes: 2 additions & 2 deletions docs/src/tutorials/input_component.md
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Expand Up @@ -10,7 +10,7 @@ This tutorial demonstrate each case and explain the pros and cons of each.

## `TimeVaryingFunction` Component

The `ModelingToolkitStandardLibrary.Blocks.TimeVaryingFunction` component is easy to use and is performative. However the data is locked to the `ODESystem` and can only be changed by building a new `ODESystem`. Therefore, running a batch of data would not be efficient. Below is an example of how to use the `TimeVaryingFunction` with `DataInterpolations` to build the function from sampled discrete data.
The `ModelingToolkitStandardLibrary.Blocks.TimeVaryingFunction` component is easy to use and is performant. However the data is locked to the `ODESystem` and can only be changed by building a new `ODESystem`. Therefore, running a batch of data would not be efficient. Below is an example of how to use the `TimeVaryingFunction` with `DataInterpolations` to build the function from sampled discrete data.

```julia
using ModelingToolkit
Expand Down Expand Up @@ -86,7 +86,7 @@ sys = structural_simplify(system)
prob = ODEProblem(sys, [], (0, time[end]))

rdata[] = data1
sol1 = solve(prob, ImplicitEuler(); dt, adaptive = false);
sol1 = solve(prob, ImplicitEuler(); dt, adaptive = false)
ddx1 = sol1[sys.ddx]

rdata[] = data2
Expand Down