diff --git a/docs/make.jl b/docs/make.jl index f850f3e76..3515c9c99 100644 --- a/docs/make.jl +++ b/docs/make.jl @@ -27,14 +27,18 @@ makedocs(modules = [ "http://www.radford.edu/~thompson/vodef90web/problems/demosnodislin/Demos_Pitagora/DemoHires/demohires.pdf", "https://www.radford.edu/%7Ethompson/RP/nonnegative.pdf", "http://www.radford.edu/~thompson/vodef90web/problems/demosnodislin/Demos_Pitagora/DemoOrego/demoorego.pdf", + "https://zenodo.org/record/5883749#.Yg-d698xmu4", + "https://www.sciencedirect.com/science/article/abs/pii/S0375960109009591", + "https://www.sciencedirect.com/science/article/abs/pii/0375960176901018", ], strict = [ :doctest, :linkcheck, :parse_error, :example_block, + :cross_references, # Other available options are - # :autodocs_block, :cross_references, :docs_block, :eval_block, :example_block, :footnote, :meta_block, :missing_docs, :setup_block + # :autodocs_block, :docs_block, :eval_block, :example_block, :footnote, :meta_block, :missing_docs, :setup_block ], doctest = false, clean = true, format = Documenter.HTML(analytics = "UA-90474609-3", diff --git a/docs/src/basics/faq.md b/docs/src/basics/faq.md index 09fb1be28..b7a36b621 100644 --- a/docs/src/basics/faq.md +++ b/docs/src/basics/faq.md @@ -443,7 +443,7 @@ like BigFloats or [ArbFloats.jl](https://github.com/JuliaArbTypes/ArbFloats.jl). #### Native Julia solvers compatibility with autodifferentiation Yes, they are compatible with automatic differentiation! Take a look at the -[sensitivity analysis](@ref sensitivity) page for more details. +[sensitivity analysis](https://docs.sciml.ai/SciMLSensitivity/stable/) page for more details. If the algorithm does not have differentiation of parameter-dependent events, then you simply need to make the initial condition have elements of Dual numbers. diff --git a/docs/src/solvers/sde_solve.md b/docs/src/solvers/sde_solve.md index 8243b908a..4fa12c8fa 100644 --- a/docs/src/solvers/sde_solve.md +++ b/docs/src/solvers/sde_solve.md @@ -43,7 +43,7 @@ a given SDE, the use of high weak order solvers is recommended. Specifically, Runge-Kutta solvers with weak order 2 due to Roessler are adaptive. All other high weak order solvers currently require a fixed step size. -## Special Noise Forms +## [Special Noise Forms](@id special_noise_forms) Some solvers are for specialized forms of noise. Diagonal noise is the default setup. Non-diagonal noise is specified via setting `noise_rate_prototype` to @@ -69,7 +69,7 @@ I(h) = \int_0^h\int_0^sdW^1_tdW^2_s in these schemes. The approximation of these iterated integrals can be avoided, if the diffusion matrix -satisfies the special commutativity condition given [above](@ref special noise forms). +satisfies the special commutativity condition given [above](@ref special_noise_forms). Because of this, many methods are only applicable to problems that satisfy the commutativity condition. In other words, many methods can only handle specific noise cases, like diagonal noise or commutative noise, because of how this iterated integral approximation diff --git a/docs/src/tutorials/sde_example.md b/docs/src/tutorials/sde_example.md index 9fc3605e4..d1f6a7083 100644 --- a/docs/src/tutorials/sde_example.md +++ b/docs/src/tutorials/sde_example.md @@ -350,4 +350,4 @@ SDEProblem(f, g, ones(2), 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](@ref financial_models). +[financial modeling tools](https://github.com/SciML/DiffEqFinancial.jl).