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add note on time integration methods to the docs #254

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Oct 27, 2020
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add note on time integration methods to the docs #254

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79f1f29
add note on time integration methods to the docs
ranocha Oct 27, 2020
e35cec9
fix typos in docs [ci skip]
ranocha Oct 27, 2020
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4 changes: 2 additions & 2 deletions README.md
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Expand Up @@ -30,8 +30,8 @@ installation and postprocessing procedures. Its features include:
* Entropy-stable shock capturing
* Positivity-preserving limiting
* Compatible with the [SciML ecosystem for ordinary differential equations](https://diffeq.sciml.ai/latest/)
* [Explicit low-storage Runge-Kutta time integration](https://diffeq.sciml.ai/latest/solvers/ode_solve/#Explicit-Strong-Stability-Preserving-Runge-Kutta-Methods-for-Hyperbolic-PDEs-(Conservation-Laws))
* [Strong stability preserving methods](https://diffeq.sciml.ai/latest/solvers/ode_solve/#Low-Storage-Methods)
* [Explicit low-storage Runge-Kutta time integration](https://diffeq.sciml.ai/latest/solvers/ode_solve/#Low-Storage-Methods)
* [Strong stability preserving methods](https://diffeq.sciml.ai/latest/solvers/ode_solve/#Explicit-Strong-Stability-Preserving-Runge-Kutta-Methods-for-Hyperbolic-PDEs-(Conservation-Laws))
* CFL-based and error-based time step control
* Square/cubic domains with periodic and weakly-enforced boundary conditions
* Multiple governing equations:
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1 change: 1 addition & 0 deletions docs/make.jl
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Expand Up @@ -39,6 +39,7 @@ makedocs(
pages = [
"Home" => "index.md",
"Conventions" => "conventions.md",
"Time integration" => "time_integration.md",
"Development" => "development.md",
"Visualization" => "visualization.md",
"Style guide" => "styleguide.md",
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16 changes: 16 additions & 0 deletions docs/src/time_integration.md
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@@ -0,0 +1,16 @@
# Time integration methods

Trixi is compatible with the [SciML ecosystem for ordinary differential equations](https://diffeq.sciml.ai/latest/).
In particular, explicit Runge-Kutta methods from [OrdinaryDiffEq.jl](https://github.com/SciML/OrdinaryDiffEq.jl)
are tested extensively.
Interesting classes of time integration schemes are
- [Explicit low-storage Runge-Kutta methods](https://diffeq.sciml.ai/latest/solvers/ode_solve/#Low-Storage-Methods)
- [Strong stability preserving methods](https://diffeq.sciml.ai/latest/solvers/ode_solve/#Explicit-Strong-Stability-Preserving-Runge-Kutta-Methods-for-Hyperbolic-PDEs-(Conservation-Laws))

!!! note

If you use explicit Runge-Kutta methods from [OrdinaryDiffEq.jl](https://github.com/SciML/OrdinaryDiffEq.jl),
the total number of `rhs!` calls can be (slightly) bigger than the number of steps times the number
of stages, e.g. to allow for interpolation (dense output), root-finding for continuous callbacks,
and error-based time step control. In general, you often should not need to worry about this if you
use Trixi.