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docs/src/jlesc17.md

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@@ -45,21 +45,25 @@ $$ b(t_0, X_0, t_N, X_N) = 0 $$
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$$ g(t_i, X_i, U_i) = 0,\quad i = 0,\dots,N $$
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- SIMD parallelism ($f0$, $f$, $g$) + sparsity: Kernels for GPU ([KernelAbstraction.jl](XXXX)) and sparse linear algebra ([CUDSS.jl](https://github.com/exanauts/CUDSS.jl))
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- SIMD parallelism ($f0$, $f$, $g$) + sparsity: Kernels for GPU ([KernelAbstraction.jl](https://juliagpu.github.io/KernelAbstractions.jl/stable/)) and sparse linear algebra ([CUDSS.jl](https://github.com/exanauts/CUDSS.jl))
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- Modelling and optimising for GPU: [ExaModels.jl](https://exanauts.github.io/ExaModels.jl/dev/guide) + [MadNLP.jl](https://madnlp.github.io/MadNLP.jl), with **built-in AD**
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- [Simple example, DSL](@ref tutorial-double-integrator-energy)
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- Compile into an ExaModel (one pass compiler, [syntax + semantics](XXXX))
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- Compile into an ExaModel (one pass compiler, [syntax + semantics](https://github.com/control-toolbox/CTParser.jl/blob/20c6be5c953587fef10b054a95f9dc8c66b90577/src/onepass.jl#L145))
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- Simple example, generated code
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```julia
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XXXX
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```
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- **Remark.** Automated scalarisation of (linear) range constraints
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```julia
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XXXX
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```
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- Solving (MadNLP + CUDSS)
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```julia
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XXXX
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```
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- [Goddard problem](https://control-toolbox.org/Tutorials.jl/stable/tutorial-goddard/)
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```julia
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XXXX
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```
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## Wrap up
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## Future
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- New [applications](XXXX) (space mechanics, biology, quantum mechanics and more)
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- New applications (space mechanics, biology, quantum mechanics and more)
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- Additional solvers: benchmarking on CPU / GPU for optimisation, Hamiltonian shooting and pathfollowing
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- Improved AD: collab between Argonne and Inria, [JLESC Shared Infra AD](https://jlesc.github.io/projects/shared_infra_ad) project (XXXX team)...
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- Improved AD: collab between Argonne and Inria, [JLESC Shared Infra AD](https://jlesc.github.io/projects/shared_infra_ad) project...
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- ... and open to contributions! If you like the package, please give us a star ⭐️
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```@raw html

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