diff --git a/docs/src/tutorials/ode_example.md b/docs/src/tutorials/ode_example.md index 6e747a9557..ad4464f93b 100644 --- a/docs/src/tutorials/ode_example.md +++ b/docs/src/tutorials/ode_example.md @@ -73,7 +73,7 @@ sol = solve(prob) ``` The solvers can be controlled using the available options are described on the -[Common Solver Options manual page](../basics/common_solver_opts.html). For example, +[Common Solver Options manual page](../../basics/common_solver_opts.html). For example, we can lower the relative tolerance (in order to get a more correct result, at the cost of more timesteps) by using the command `reltol`: @@ -144,7 +144,7 @@ In DifferentialEquations.jl, some good "go-to" choices for ODEs are: - `CVODE_BDF()` for stiff equations on `Vector{Float64}`. For a comprehensive list of the available algorithms and detailed recommendations, -[Please see the solver documentation](../solvers/ode_solve.html). Every problem +[Please see the solver documentation](../../solvers/ode_solve.html). Every problem type has an associated page detailing all of the solvers associated with the problem. ### Step 3: Analyzing the Solution @@ -191,11 +191,11 @@ step, while `(t)` is an interpolation at time `t`! If in the solver `dense=true` (this is the default unless `saveat` is used), then this interpolation is a high order interpolation and thus usually matches the error of the solution time points. The interpolations associated with each solver -is [detailed at the solver algorithm page](../solvers/ode_solve.html). If `dense=false` +is [detailed at the solver algorithm page](../../solvers/ode_solve.html). If `dense=false` (unless specifically set, this only occurs when `save_everystep=false` or `saveat` is used) then this defaults to giving a linear interpolation. -For details on more handling the output, see [the solution handling page](../basics/solution.html). +For details on more handling the output, see [the solution handling page](../../basics/solution.html). #### Plotting Solutions @@ -220,7 +220,7 @@ gui() ``` The plot function can be formatted using [the attributes available in Plots.jl](https://juliaplots.github.io/). -Additional DiffEq-specific controls are documented [at the plotting page](../basics/plot.html). +Additional DiffEq-specific controls are documented [at the plotting page](../../basics/plot.html). For example, from the Plots.jl attribute page we see that the line width can be set via the argument `linewidth`. Additionally, a title can be set with `title`. @@ -463,11 +463,11 @@ In many cases, the common workflow only starts with solving the differential equ Many common setups have built-in solutions in DifferentialEquations.jl. For example, check out the features for: -- [Handling, parallelizing, and analyzing large Monte Carlo experiments](../features/monte_carlo.html) -- [Saving the output to tabular formats like DataFrames and CSVs](../features/io.html) -- [Event handling](../features/callback_functions.html) -- [Parameter estimation (inverse problems)](../analysis/parameter_estimation.html) -- [Quantification of numerical uncertainty and error](../analysis/uncertainty_quantification.html) +- [Handling, parallelizing, and analyzing large Monte Carlo experiments](../../features/monte_carlo.html) +- [Saving the output to tabular formats like DataFrames and CSVs](../../features/io.html) +- [Event handling](../../features/callback_functions.html) +- [Parameter estimation (inverse problems)](../../analysis/parameter_estimation.html) +- [Quantification of numerical uncertainty and error](../../analysis/uncertainty_quantification.html) Many more are defined in the relevant sections of the docs. Please explore the rest of the documentation, including tutorials for getting started with other types diff --git a/docs/src/tutorials/rode_example.md b/docs/src/tutorials/rode_example.md index 7e58876836..e937fa0455 100644 --- a/docs/src/tutorials/rode_example.md +++ b/docs/src/tutorials/rode_example.md @@ -28,7 +28,7 @@ sol = solve(prob,RandomEM(),dt=1/100) The random process defaults to a Gaussian/Wiener process, so there is nothing else required here! See the documentation on -[`NoiseProcess`es](../features/noise_process.html) for details on how to define +[`NoiseProcess`es](../../features/noise_process.html) for details on how to define other noise proceses. ## Example 2: Systems of RODEs diff --git a/docs/src/tutorials/sde_example.md b/docs/src/tutorials/sde_example.md index 5141ac1835..6864487968 100644 --- a/docs/src/tutorials/sde_example.md +++ b/docs/src/tutorials/sde_example.md @@ -108,7 +108,7 @@ constructor: monte_prob = MonteCarloProblem(prob) ``` -The solver commands are defined [at the Monte Carlo page](../features/monte_carlo.html). +The solver commands are defined [at the Monte Carlo page](../../features/monte_carlo.html). For example we can choose to have 1000 trajectories via `num_monte=1000`. In addition, this will automatically parallelize using Julia native parallelism if extra processes are added via `addprocs()`, but we can change this to use multithreading via @@ -254,7 +254,7 @@ multiplication. ## Example 4: Colored Noise -Colored noise can be defined [using the Noise Process interface](../features/noise_process.html). +Colored noise can be defined [using the Noise Process interface](../../features/noise_process.html). In that portion of the docs, it is shown how to define your own noise process `my_noise`, which can be passed to the SDEProblem @@ -307,4 +307,4 @@ SDEProblem(f,g,u0,tspan,noise=heston_noise) Of course, to fully define this problem we need to define our constants. Constructors for making common models like this easier to define can be found in the modeling toolkits. For example, the `HestonProblem` is pre-defined as part of the -[financial modeling tools](../models/financial.html). +[financial modeling tools](../../models/financial.html). diff --git a/docs/src/types/rode_types.md b/docs/src/types/rode_types.md index 004a191cba..54f75aef12 100644 --- a/docs/src/types/rode_types.md +++ b/docs/src/types/rode_types.md @@ -27,7 +27,7 @@ Defines the RODE with the specified functions. The default noise is `WHITE_NOISE * `tspan`: The timespan for the problem. * `noise`: The noise process applied to the noise upon generation. Defaults to Gaussian white noise. For information on defining different noise processes, - see [the noise process documentation page](../features/noise_process.html) + see [the noise process documentation page](../../features/noise_process.html) * `noise_prototype`: A prototype type instance for the noise vector. It defaults to `nothing`, which means the problem should be interpreted as having a noise vector whose size matches `u0`. diff --git a/docs/src/types/sde_types.md b/docs/src/types/sde_types.md index 57c63f9266..31992d3eb0 100644 --- a/docs/src/types/sde_types.md +++ b/docs/src/types/sde_types.md @@ -38,7 +38,7 @@ Defines the SDE with the specified functions. The default noise is `WHITE_NOISE` * `tspan`: The timespan for the problem. * `noise`: The noise process applied to the noise upon generation. Defaults to Gaussian white noise. For information on defining different noise processes, - see [the noise process documentation page](../features/noise_process.html) + see [the noise process documentation page](../../features/noise_process.html) * `noise_rate_prototype`: A prototype type instance for the noise rates, that is the output `g`. It can be any type which overloads `A_mul_B!` with itself being the middle argument. Commonly, this is a matrix or sparse matrix. If