Update paper.md (#32)

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paper/paper.md

@@ -67,7 +67,7 @@ where $\mathcal{T} = [t_0, t_f]$ is the fixed time horizon and $x : \mathcal{T}
 
 For more information on optimal experimental design for DAEs and their sensitivity analysis, we refer to [@Koerkel2002; @Li2000SensitivityAnalysisDifferential]. 
 
-The functionality in this package integrates into Julia's [`SciML`](https://sciml.ai/) ecosystem. The model is provided in symbolic form as an `ODESystem` using `ModelingToolkit.jl`[@ma2021modelingtoolkit] with additional frequency information for the observed and control variables. Both ODE or DAE systems can be provided. `DynamicOED.jl` augments the given system symbolically with its sensitivity equations and the dynamics of the FIM. The resulting system together with a sufficient information criterion defines an `OEDProblem`, solveable using `DifferentialEquations.jl` [@rackauckas2017]. Here, all sampling and control decisions are discretized in time and can be used to model additional constraints. At last, the `OEDProblem` can be transformed into an `OptimizationProblem` as a sufficient input to `Optimization.jl` [@vaibhav_kumar_dixit_2023_7738525]. Here, a variety of optimization solvers for nonlinear programming and mixed-integer nonlinear programming available as additional backends, e.g. `Juniper` [@juniper] or `Ipopt` [@Waechter2006]. A simple example demonstrates the usage of `DynamicOED.jl` for the Lotka-Volterra system [@Sager2013]. 
+The functionality in this package integrates into Julia's [`SciML`](https://sciml.ai/) ecosystem. The model is provided in symbolic form as an `ODESystem` using `ModelingToolkit.jl` [@ma2021modelingtoolkit] with additional frequency information for the observed and control variables. Both ODE or DAE systems can be provided. `DynamicOED.jl` augments the given system symbolically with its sensitivity equations and the dynamics of the FIM. The resulting system together with a sufficient information criterion defines an `OEDProblem`, solveable using `DifferentialEquations.jl` [@rackauckas2017]. Here, all sampling and control decisions are discretized in time and can be used to model additional constraints. At last, the `OEDProblem` can be transformed into an `OptimizationProblem` as a sufficient input to `Optimization.jl` [@vaibhav_kumar_dixit_2023_7738525]. Here, a variety of optimization solvers for nonlinear programming and mixed-integer nonlinear programming available as additional backends, e.g. `Juniper` [@juniper] or `Ipopt` [@Waechter2006]. A simple example demonstrates the usage of `DynamicOED.jl` for the Lotka-Volterra system [@Sager2013]. 
 
 \autoref{fig:lotka} shows the solution of the example above including the differential states, sensitivities $G$ and the sampling decisions $w$. More examples can be found in the [documentation](https://mathopt.github.io/DynamicOED.jl/dev/). 
 
@@ -133,4 +133,4 @@ The work was funded by the German Research Foundation DFG within the priority
 program 2331 'Machine Learning in Chemical Engineering' under grants KI 417/9-1, SA
 2016/3-1, SE 586/25-1
 
-# References
+# References