SciML · ChrisRackauckas · Oct 14, 2024 · Oct 13, 2024 · Oct 13, 2024
diff --git a/README.md b/README.md
@@ -4,4 +4,3 @@
 [![Documentation and Deployment](https://badge.buildkite.com/b4720f5f3a8d2d1ac8bbaebf5e0e38aaae28b6c8fb436ba05e.svg)](https://buildkite.com/julialang/scimldocs?branch=main)
 
 Attempts at a single documentation for the full SciML interface.
-
diff --git a/docs/make.jl b/docs/make.jl
@@ -8,76 +8,74 @@ ENV["GKSwstype"] = "100"
 using Plots
 
 mathengine = MathJax3(Dict(:loader => Dict("load" => ["[tex]/require", "[tex]/mathtools"]),
-                           :tex => Dict("inlineMath" => [["\$", "\$"], ["\\(", "\\)"]],
-                                        "packages" => [
-                                            "base",
-                                            "ams",
-                                            "autoload",
-                                            "mathtools",
-                                            "require",
-                                        ])))
+    :tex => Dict("inlineMath" => [["\$", "\$"], ["\\(", "\\)"]],
+        "packages" => [
+            "base",
+            "ams",
+            "autoload",
+            "mathtools",
+            "require"
+        ])))
 
 makedocs(sitename = "Overview of Julia's SciML",
-         authors = "Chris Rackauckas",
-         modules = Module[],
-         clean = true, doctest = false, linkcheck = true,
-         linkcheck_ignore = ["https://twitter.com/ChrisRackauckas/status/1477274812460449793",
-                             "https://epubs.siam.org/doi/10.1137/0903023",
-                             "https://bkamins.github.io/julialang/2020/12/24/minilanguage.html",
-                             "https://arxiv.org/abs/2109.06786",
-                             "https://arxiv.org/abs/2001.04385",
-
-    ],
-         format = Documenter.HTML(assets = ["assets/favicon.ico"],
-                                  canonical = "https://docs.sciml.ai/stable/",
-                                  mathengine = mathengine),
-         pages = [
-             "SciML: Open Source Software for Scientific Machine Learning with Julia" => "index.md",
-             "Getting Started" => [
-                 "getting_started/getting_started.md",
-                 "New User Tutorials" => [
-                     "getting_started/installation.md",
-                     "getting_started/first_simulation.md",
-                     "getting_started/first_optimization.md",
-                     "getting_started/fit_simulation.md",
-                     "getting_started/find_root.md",
-                 ],
-                 "Comparison With Other Tools" => [
-                     "comparisons/python.md",
-                     "comparisons/matlab.md",
-                     "comparisons/r.md",
-                     "comparisons/cppfortran.md",
-                 ],
-             ],
-             "Showcase of Cool Examples" => Any["showcase/showcase.md",
-                                                "Automated Model Discovery" => Any["showcase/missing_physics.md",
-                                                                                   "showcase/bayesian_neural_ode.md",
-                                                                                   "showcase/blackhole.md"],
-                                                "Solving Difficult Equations Efficiently" => Any["showcase/brusselator.md",
-                                                                                                 "showcase/pinngpu.md",
-                                                                                                 "showcase/massively_parallel_gpu.md",
-                                                                                                 "showcase/gpu_spde.md"],
-                                                "Useful Cool Wonky Things" => Any["showcase/ode_types.md",
-                                                                                  "showcase/symbolic_analysis.md",
-                                                                                  "showcase/optimization_under_uncertainty.md"]],
-             "What is SciML?" => ["overview.md",
-                 "Solvers" => ["highlevels/equation_solvers.md",
-                     "highlevels/inverse_problems.md",
-                     "highlevels/partial_differential_equation_solvers.md"],
-                 "Modeling Tools" => ["highlevels/modeling_languages.md",
-                     "highlevels/model_libraries_and_importers.md",
-                     "highlevels/symbolic_tools.md",
-                     "highlevels/array_libraries.md"],
-                 "Simulation Analysis" => ["highlevels/parameter_analysis.md",
-                     "highlevels/uncertainty_quantification.md",
-                     "highlevels/plots_visualization.md"],
-                 "Machine Learning" => ["highlevels/function_approximation.md",
-                     "highlevels/implicit_layers.md",
-                     "highlevels/symbolic_learning.md"],
-                 "Developer Tools" => ["highlevels/numerical_utilities.md",
-                     "highlevels/interfaces.md",
-                     "highlevels/developer_documentation.md"],
-                 "Extra Learning Resources" => ["highlevels/learning_resources.md"],
-             ]])
+    authors = "Chris Rackauckas",
+    modules = Module[],
+    clean = true, doctest = false, linkcheck = true,
+    linkcheck_ignore = ["https://twitter.com/ChrisRackauckas/status/1477274812460449793",
+        "https://epubs.siam.org/doi/10.1137/0903023",
+        "https://bkamins.github.io/julialang/2020/12/24/minilanguage.html",
+        "https://arxiv.org/abs/2109.06786",
+        "https://arxiv.org/abs/2001.04385"],
+    format = Documenter.HTML(assets = ["assets/favicon.ico"],
+        canonical = "https://docs.sciml.ai/stable/",
+        mathengine = mathengine),
+    pages = [
+        "SciML: Open Source Software for Scientific Machine Learning with Julia" => "index.md",
+        "Getting Started" => [
+            "getting_started/getting_started.md",
+            "New User Tutorials" => [
+                "getting_started/installation.md",
+                "getting_started/first_simulation.md",
+                "getting_started/first_optimization.md",
+                "getting_started/fit_simulation.md",
+                "getting_started/find_root.md"
+            ],
+            "Comparison With Other Tools" => [
+                "comparisons/python.md",
+                "comparisons/matlab.md",
+                "comparisons/r.md",
+                "comparisons/cppfortran.md"
+            ]
+        ],
+        "Showcase of Cool Examples" => Any["showcase/showcase.md",
+            "Automated Model Discovery" => Any["showcase/missing_physics.md",
+                "showcase/bayesian_neural_ode.md",
+                "showcase/blackhole.md"],
+            "Solving Difficult Equations Efficiently" => Any["showcase/brusselator.md",
+                "showcase/pinngpu.md",
+                "showcase/massively_parallel_gpu.md",
+                "showcase/gpu_spde.md"],
+            "Useful Cool Wonky Things" => Any["showcase/ode_types.md",
+                "showcase/symbolic_analysis.md",
+                "showcase/optimization_under_uncertainty.md"]],
+        "What is SciML?" => ["overview.md",
+            "Solvers" => ["highlevels/equation_solvers.md",
+                "highlevels/inverse_problems.md",
+                "highlevels/partial_differential_equation_solvers.md"],
+            "Modeling Tools" => ["highlevels/modeling_languages.md",
+                "highlevels/model_libraries_and_importers.md",
+                "highlevels/symbolic_tools.md",
+                "highlevels/array_libraries.md"],
+            "Simulation Analysis" => ["highlevels/parameter_analysis.md",
+                "highlevels/uncertainty_quantification.md",
+                "highlevels/plots_visualization.md"],
+            "Machine Learning" => ["highlevels/function_approximation.md",
+                "highlevels/implicit_layers.md",
+                "highlevels/symbolic_learning.md"],
+            "Developer Tools" => ["highlevels/numerical_utilities.md",
+                "highlevels/interfaces.md",
+                "highlevels/developer_documentation.md"],
+            "Extra Learning Resources" => ["highlevels/learning_resources.md"]
+        ]])
 
 deploydocs(repo = "github.com/SciML/SciMLDocs")
diff --git a/docs/make_aggregate.jl b/docs/make_aggregate.jl
@@ -28,8 +28,9 @@ docsmodules = [
         =#
     ],
     "Solvers" => [
-        "Equation Solvers" => ["LinearSolve", "NonlinearSolve", "DiffEqDocs", "Integrals", "DifferenceEquations",
-            "Optimization", "JumpProcesses"],
+        "Equation Solvers" => [
+            "LinearSolve", "NonlinearSolve", "DiffEqDocs", "Integrals",
+            "DifferenceEquations", "Optimization", "JumpProcesses", "LineSearch"],
         #=
         "Third-Party Equation Solvers" => [
             "LowRankIntegrators",

diff --git a/docs/src/comparisons/matlab.md b/docs/src/comparisons/matlab.md
@@ -74,10 +74,10 @@ The following chart will help you get quickly acquainted with Julia's SciML Tool
 |:------------------- |:------------------------------------------------------------------------------------- |
 | `plot`              | [Plots](https://docs.juliaplots.org/stable/), [Makie](https://docs.makie.org/stable/) |
 | `sparse`            | [SparseArrays](https://docs.julialang.org/en/v1/stdlib/SparseArrays/#Sparse-Arrays)   |
-| `interp1`           | [DataInterpolations](https://docs.sciml.ai/DataInterpolations/)                |
-| `\`, `gmres`, `cg`  | [LinearSolve](http://linearsolve.sciml.ai/dev/)                                       |
+| `interp1`           | [DataInterpolations](https://docs.sciml.ai/DataInterpolations/)                       |
+| `\`, `gmres`, `cg`  | [LinearSolve](https://linearsolve.sciml.ai/dev/)                                      |
 | `fsolve`            | [NonlinearSolve](https://nonlinearsolve.sciml.ai/)                                    |
-| `quad`              | [Integrals](https://docs.sciml.ai/Integrals/stable/)                                              |
+| `quad`              | [Integrals](https://docs.sciml.ai/Integrals/stable/)                                  |
 | `fmincon`           | [Optimization](https://optimization.sciml.ai/)                                        |
 | `odeXX`             | [DifferentialEquations](https://diffeq.sciml.ai/latest/)                              |
 | `ode45`             | `Tsit5`                                                                               |

diff --git a/docs/src/comparisons/python.md b/docs/src/comparisons/python.md
@@ -61,7 +61,7 @@ The following chart will help you get quickly acquainted with Julia's SciML Tool
 |:---------------------------- |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | Matplotlib                   | [Plots](https://docs.juliaplots.org/stable/), [Makie](https://docs.makie.org/stable/)                                                                                   |
 | `scipy.special`              | [SpecialFunctions](https://github.com/JuliaMath/SpecialFunctions.jl)                                                                                                    |
-| `scipy.linalg.solve`         | [LinearSolve](http://linearsolve.sciml.ai/dev/)                                                                                                                         |
+| `scipy.linalg.solve`         | [LinearSolve](https://linearsolve.sciml.ai/dev/)                                                                                                                        |
 | `scipy.integrate`            | [Integrals](https://docs.sciml.ai/Integrals/stable/)                                                                                                                    |
 | `scipy.optimize`             | [Optimization](https://optimization.sciml.ai/)                                                                                                                          |
 | `scipy.optimize.fsolve`      | [NonlinearSolve](https://nonlinearsolve.sciml.ai/)                                                                                                                      |
@@ -71,7 +71,7 @@ The following chart will help you get quickly acquainted with Julia's SciML Tool
 | `scipy.sparse`               | [SparseArrays](https://docs.julialang.org/en/v1/stdlib/SparseArrays/#Sparse-Arrays), [ARPACK](https://github.com/JuliaLinearAlgebra/Arpack.jl)                          |
 | `odeint`/`solve_ivp`         | [DifferentialEquations](https://diffeq.sciml.ai/latest/)                                                                                                                |
 | `scipy.integrate.solve_bvp`  | [Boundary-value problem](https://diffeq.sciml.ai/latest/tutorials/bvp_example/#Boundary-Value-Problems)                                                                 |
-| PyTorch                      | [Flux](https://fluxml.ai/), [Lux](http://lux.csail.mit.edu/stable/)                                                                                                     |
+| PyTorch                      | [Flux](https://fluxml.ai/), [Lux](https://lux.csail.mit.edu/stable/)                                                                                                    |
 | gillespy2                    | [Catalyst](https://catalyst.sciml.ai/dev/), [JumpProcesses](https://github.com/SciML/JumpProcesses.jl)                                                                  |
 | scipy.optimize.approx_fprime | [FiniteDiff](https://github.com/JuliaDiff/FiniteDiff.jl)                                                                                                                |
 | autograd                     | [ForwardDiff\*](https://github.com/JuliaDiff/ForwardDiff.jl), [Enzyme\*](https://github.com/EnzymeAD/Enzyme.jl), [DiffEqSensitivity](https://sensitivity.sciml.ai/dev/) |

diff --git a/docs/src/getting_started/find_root.md b/docs/src/getting_started/find_root.md
@@ -56,7 +56,7 @@ prob = NonlinearProblem(ns, [])
 sol = solve(prob, NewtonRaphson())
 
 # Analyze the solution
-@show sol[[x,y,z]], sol.resid
+@show sol[[x, y, z]], sol.resid
 ```
 
 ## Step-by-Step Solution
@@ -178,5 +178,5 @@ We can check it as follows:
 
 ```@example first_rootfind
 # Analyze the solution
-@show sol[[x,y,z]], sol.resid
+@show sol[[x, y, z]], sol.resid
 ```
diff --git a/docs/src/getting_started/first_optimization.md b/docs/src/getting_started/first_optimization.md
@@ -15,11 +15,11 @@ Let's solve our first optimization problem!
 
 The following parts of the SciML Ecosystem will be used in this tutorial:
 
-| Module                                                                                         | Description                        |
-|:---------------------------------------------------------------------------------------------- |:---------------------------------- |
-| [Optimization.jl](https://docs.sciml.ai/Optimization/stable/)                                  | The numerical optimization package |
-| [OptimizationNLopt.jl](https://docs.sciml.ai/Optimization/stable/optimization_packages/nlopt/) | The NLopt optimizers we will use   |
-| [ForwardDiff.jl](https://docs.sciml.ai/Optimization/stable/API/optimization_function/#Optimization.AutoForwardDiff) | The automatic differentiation library for gradients  |
+| Module                                                                                                              | Description                                         |
+|:------------------------------------------------------------------------------------------------------------------- |:--------------------------------------------------- |
+| [Optimization.jl](https://docs.sciml.ai/Optimization/stable/)                                                       | The numerical optimization package                  |
+| [OptimizationNLopt.jl](https://docs.sciml.ai/Optimization/stable/optimization_packages/nlopt/)                      | The NLopt optimizers we will use                    |
+| [ForwardDiff.jl](https://docs.sciml.ai/Optimization/stable/API/optimization_function/#Optimization.AutoForwardDiff) | The automatic differentiation library for gradients |
 
 ## Problem Setup
 
@@ -68,9 +68,9 @@ To do this tutorial, we will need a few components:
 
   - [Optimization.jl](https://docs.sciml.ai/Optimization/stable/), the optimization interface.
   - [OptimizationNLopt.jl](https://docs.sciml.ai/Optimization/stable/optimization_packages/nlopt/), the optimizers we will use.
-  - [ForwardDiff.jl](https://docs.sciml.ai/Optimization/stable/API/optimization_function/#Optimization.AutoForwardDiff), 
+  - [ForwardDiff.jl](https://docs.sciml.ai/Optimization/stable/API/optimization_function/#Optimization.AutoForwardDiff),
     the automatic differentiation library for gradients
-    
+
 Note that Optimization.jl is an interface for optimizers, and thus we always have to choose
 which optimizer we want to use. Here we choose to demonstrate `OptimizationNLopt` because
 of its efficiency and versatility. But there are many other possible choices. Check out
@@ -102,6 +102,7 @@ parameters, and write out the loss function on a vector-defined state as follows
 # Define the problem to optimize
 L(u, p) = (p[1] - u[1])^2 + p[2] * (u[2] - u[1]^2)^2
 ```
+
 Next we need to create an `OptimizationFunction` where we tell Optimization.jl to use the ForwardDiff.jl
 package for creating the gradient and other derivatives required by the optimizer.
 

diff --git a/docs/src/getting_started/first_simulation.md b/docs/src/getting_started/first_simulation.md
@@ -173,9 +173,9 @@ to represent an `ODESystem` with the following:
 @mtkbuild sys = ODESystem(eqs, t)
 ```
 
-Notice that in our equations we have an algebraic equation `z ~ x + y`. This is not a 
-differential equation but an algebraic equation, and thus we call this set of equations a 
-Differential-Algebraic Equation (DAE). The symbolic system of ModelingToolkit can eliminate 
+Notice that in our equations we have an algebraic equation `z ~ x + y`. This is not a
+differential equation but an algebraic equation, and thus we call this set of equations a
+Differential-Algebraic Equation (DAE). The symbolic system of ModelingToolkit can eliminate
 such equations to return simpler forms to numerically approximate.
 
 Notice that what is returned is an `ODESystem`, but now with the simplified set of
Original file line number	Diff line number	Diff line change
Expand Up		@@ -4,4 +4,3 @@
		[![Documentation and Deployment](https://badge.buildkite.com/b4720f5f3a8d2d1ac8bbaebf5e0e38aaae28b6c8fb436ba05e.svg)](https://buildkite.com/julialang/scimldocs?branch=main)

		Attempts at a single documentation for the full SciML interface.