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‎class03/class03.md‎

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Add notes, links, and resources below.
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Theory content is available [here](Class03_Chapter_version1.pdf)
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The notebook for this class is available [here](https://learningtooptimize.github.io/LearningToControlClass/dev/class03/class3_pluto_notebook.html).
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Theory slides are available [here](Class03_Theory.pdf).
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‎class04/class04.md‎

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Joaquim Dias Garcia's Guest Lecture for the Optimal Control & Learning Course.
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The lecture covered Multistage Stochastic Programming and Stochastic Dual Dynamic Programming (SDDP).
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[Video Lecture Link](https://www.youtube.com/watch?v=jCkHtvTe7tU&t=5382s)
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‎class06/class06.md‎

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Henrique Ferrolho's Guest Lecture for the Optimal Control & Learning Course.
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The lecture covered Nonlinear trajectory optimization for robots in Julia.
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[Video Lecture Link](https://www.youtube.com/watch?v=rMo_o1j7ukM)
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‎class07/class07.md‎

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# Class 7 — 10/03/2025
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**Presenter:** TBD
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**Presenter:** Jouke van Westrenen
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**Topic:** Essentials of PDEs for control engineers; weak forms; FEM/FDM review
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Files for this class are available [here](https://github.com/LearningToOptimize/LearningToControlClass/tree/main/class07).

‎class09/class09.md‎

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François Pacaud's Guest Lecture for the Optimal Control & Learning Course.
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The lecture covered Nonlinear Programming theory, the fundamentals and practical lessons of GPU-accelerated optimization, and applications in optimal control.
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[Video Lecture Link](https://www.youtube.com/watch?v=fhCo3UYmbFc)

‎class10/class10.md‎

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**Topic:** Neural Differential Equations: classical solvers + adjoint methods
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https://book.sciml.ai/notes/11-Differentiable_Programming_and_Neural_Differential_Equations/
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Chris Rackauckas's Guest Lecture for the Optimal Control & Learning Course.
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The lecture covered Neural Differential Equations: Classical Solvers and Adjoint Methods.
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[Video Lecture Link](https://www.youtube.com/watch?v=hkYIlXX9q5A)
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[Lecture Notes](https://book.sciml.ai/notes/11-Differentiable_Programming_and_Neural_Differential_Equations/)
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‎class13/class13.md‎

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Charlelie Laurent's Guest Lecture for the Optimal Control & Learning Course.
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The lecture explored the NVIDIA PhysicsNeMo framework, highlighting its use in training AI surrogates to accelerate physics simulations. It also provided a theoretical overview of how Graph Neural Networks can enhance generalization in complex settings.
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[Video Lecture Link](https://www.youtube.com/watch?v=hYOgv3Rptxk&t=28s).

‎docs/make.jl‎

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joinpath(repo_dir, "class02", "part1_root_finding.html"),
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joinpath(repo_dir, "class02", "part2_eq_constraints.html"),
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joinpath(repo_dir, "class02", "part3_ipm.html"),
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joinpath(repo_dir, "class03", "class3_pluto_notebook.html"),
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joinpath(repo_dir, "class05", "class05.html"),
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joinpath(repo_dir, "class08", "class08.html"),
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joinpath(repo_dir, "class15", "class15.html"),
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]
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if !isdir(build_dir)
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symlink(joinpath(repo_dir, "class02"),
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joinpath(repo_dir, "docs", "src", "class02")
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)
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symlink(joinpath(repo_dir, "class03"),
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joinpath(repo_dir, "docs", "src", "class03")
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)
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symlink(joinpath(repo_dir, "class05"),
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joinpath(repo_dir, "docs", "src", "class05")
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)
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symlink(joinpath(repo_dir, "class08"),
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joinpath(repo_dir, "docs", "src", "class08")
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)
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symlink(joinpath(repo_dir, "class15"),
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joinpath(repo_dir, "docs", "src", "class15")
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)
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end
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makedocs(
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"class01/background_materials/git_adventure_guide.md",
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],
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"Class 2" => "class02/overview.md",
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"Class 3" => "class03/class03.md",
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"Class 4" => "class04/class04.md",
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"Class 5" => "class05/class05.md",
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"Class 6" => "class06/class06.md",
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"Class 7" => "class07/class07.md",
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"Class 8" => "class08/class08.md",
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"Class 9" => "class09/class09.md",
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"Class 10" => "class10/class10.md",
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# "Class 11" => "class11/class11.md",
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"Class 12" => "class12/class12.md",
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"Class 13" => "class13/class13.md",
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# "Class 14" => "class14/class14.md",
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"Class 15" => "class15/class15.md",
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],
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)
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