Merge pull request SciML#2956 from contradict/fix-docs-build

Fix docs build

Author: ChrisRackauckas

docs/src/basics/InputOutput.md

@@ -37,6 +37,7 @@ This function takes a vector of variables that are to be considered inputs, i.e.
 The following example implements a simple first-order system with an input `u` and state `x`. The function `f` is generated using `generate_control_function`, and the function `f` is then tested with random input and state values.
 
 ```@example inputoutput
+using ModelingToolkit
 import ModelingToolkit: t_nounits as t, D_nounits as D
 @variables x(t)=0 u(t)=0 y(t)
 @parameters k = 1

docs/src/comparison.md

@@ -4,7 +4,7 @@
 
   - Both Modelica and ModelingToolkit.jl are acausal modeling languages.
   - Modelica is a language with many different implementations, such as
-    [Dymola](https://www.3ds.com/products-services/catia/products/dymola/) and
+    [Dymola](https://www.3ds.com/products/catia/dymola/) and
     [OpenModelica](https://openmodelica.org/), which have differing levels of
     performance and can give different results on the same model. Many of the
     commonly used Modelica compilers are not open-source. ModelingToolkit.jl

docs/src/index.md

@@ -137,7 +137,7 @@ Below is an incomplete list of extension libraries one may want to be aware of:
       + Transient solution of the CME
       + Dynamic state spaces
       + Accepts reaction systems defined using Catalyst.jl DSL.
-  - [FiniteStateProjection.jl](https://github.com/kaandocal/FiniteStateProjection.jl): High-performance simulation of
+  - [FiniteStateProjection.jl](https://github.com/SciML/FiniteStateProjection.jl): High-performance simulation of
     chemical master equations (CME) via finite state projections
 
       + Accepts reaction systems defined using Catalyst.jl DSL.

docs/src/tutorials/ode_modeling.md

@@ -229,7 +229,7 @@ Instead, the function might be provided as time-series data.
 MTK handles this situation by allowing us to “register” arbitrary Julia functions,
 which are excluded from symbolic transformations and thus used as-is.
 For example, you could interpolate given the time-series using
-[DataInterpolations.jl](https://github.com/PumasAI/DataInterpolations.jl). Here,
+[DataInterpolations.jl](https://github.com/SciML/DataInterpolations.jl). Here,
 we illustrate this option with a simple lookup ("zero-order hold") of a vector
 of random values: