SpECTRE
v2024.06.18

Criteria for deciding how a mesh should be adapted. More...
Namespaces  
namespace  OptionTags 
Option tags for AMR criteria.  
namespace  Tags 
Tags for adaptive mesh refinement criteria  
Classes  
class  Constraints 
Refine the grid towards the target constraint violation. More...  
class  DriveToTarget 
Refine the grid towards the target number of grid points and refinement levels in each dimension and then oscillate about the target. More...  
class  IncreaseResolution 
Uniformly increases the number of grid points by one. More...  
class  Loehner 
hrefine the grid based on a smoothness indicator More...  
class  Persson 
hrefine the grid based on power in the highest modes More...  
class  Random 
Randomly refine (or coarsen) an Element in each dimension. More...  
class  TruncationError 
Refine the grid towards the target truncation error. More...  
Typedefs  
template<size_t Dim, typename TensorTags >  
using  standard_criteria = tmpl::list< ::amr::Criteria::IncreaseResolution< Dim >, ::amr::Criteria::TruncationError< Dim, TensorTags >, ::amr::Criteria::Loehner< Dim, TensorTags >, ::amr::Criteria::Persson< Dim, TensorTags >, ::amr::Criteria::DriveToTarget< Dim >, ::amr::Criteria::Random > 
AMR criteria that are generally applicable. More...  
Functions  
template<size_t Dim>  
double  loehner_smoothness_indicator (gsl::not_null< DataVector * > first_deriv_buffer, gsl::not_null< DataVector * > second_deriv_buffer, const DataVector &tensor_component, const Mesh< Dim > &mesh, size_t dimension) 
Computes an anisotropic smoothness indicator based on the magnitude of second derivatives. More...  
template<size_t Dim>  
std::array< double, Dim >  loehner_smoothness_indicator (const DataVector &tensor_component, const Mesh< Dim > &mesh) 
Computes an anisotropic smoothness indicator based on the magnitude of second derivatives. More...  
template<size_t Dim>  
double  persson_smoothness_indicator (gsl::not_null< DataVector * > filtered_component_buffer, const DataVector &tensor_component, const Mesh< Dim > &mesh, size_t dimension, size_t num_highest_modes) 
Computes an anisotropic smoothness indicator based on the power in the highest modes. More...  
template<size_t Dim>  
std::array< double, Dim >  persson_smoothness_indicator (const DataVector &tensor_component, const Mesh< Dim > &mesh, size_t num_highest_modes) 
Computes an anisotropic smoothness indicator based on the power in the highest modes. More...  
Criteria for deciding how a mesh should be adapted.
using amr::Criteria::standard_criteria = typedef tmpl::list< ::amr::Criteria::IncreaseResolution<Dim>, ::amr::Criteria::TruncationError<Dim, TensorTags>, ::amr::Criteria::Loehner<Dim, TensorTags>, ::amr::Criteria::Persson<Dim, TensorTags>, ::amr::Criteria::DriveToTarget<Dim>, ::amr::Criteria::Random> 
AMR criteria that are generally applicable.
Dim  the spatial dimension 
TensorTags  the tensor fields to monitor 
std::array< double, Dim > amr::Criteria::loehner_smoothness_indicator  (  const DataVector &  tensor_component, 
const Mesh< Dim > &  mesh  
) 
Computes an anisotropic smoothness indicator based on the magnitude of second derivatives.
This smoothness indicator is simply the L2 norm of the logical second derivative of the tensor component in the given dimension
:
\begin{equation} \epsilon_k = \sqrt{\frac{1}{N_\mathrm{points}} \sum_{p=1}^N_\mathrm{points} \left(\partial^2 u / \partial \xi_k^2\right)^2} \end{equation}
If the smoothness indicator is large in a direction, meaning the tensor component has a large second derivative in that direction, the element should be hrefined. If the smoothness indicator is small, the element should be hcoarsened. A coarsing threshold of about a third of the refinement threshold seems to work well, but this will need more testing.
Note that it is not at all clear that a smoothness indicator based on the magnitude of second derivatives is useful in a DG context. Smooth functions with higherorder derivatives can be approximated just fine by higherorder DG elements without the need for hrefinement. The use of second derivatives to indicate the need for refinement originated in the finite element context with linear elements. Other smoothness indicators might prove more useful for DG elements, e.g. based on jumps or oscillations of the solution. We can also explore applying the troubledcell indicators (TCIs) used in hydrodynamic simulations as hrefinement indicators.
Specifically, this smoothness indicator is based on [125] (hence the name of the function), which is popular in the finite element community and also used in a DG context by [58], Eq. (34), and by [157], Eq. (15). We make several modifications:
In addition to the above modifications, we can consider approximating the second derivative using finite differences, as explored in the prototype https://github.com/sxscollaboration/dgcharm/blob/HpAmr/Evolution/HpAmr/LohnerRefiner.hpp. This would allow falling back to the normalization used by Löhner and might be cheaper to compute, but it requires an interpolation to the center and maybe also to the faces, depending on the desired stencil.
double amr::Criteria::loehner_smoothness_indicator  (  gsl::not_null< DataVector * >  first_deriv_buffer, 
gsl::not_null< DataVector * >  second_deriv_buffer,  
const DataVector &  tensor_component,  
const Mesh< Dim > &  mesh,  
size_t  dimension  
) 
Computes an anisotropic smoothness indicator based on the magnitude of second derivatives.
This smoothness indicator is simply the L2 norm of the logical second derivative of the tensor component in the given dimension
:
\begin{equation} \epsilon_k = \sqrt{\frac{1}{N_\mathrm{points}} \sum_{p=1}^N_\mathrm{points} \left(\partial^2 u / \partial \xi_k^2\right)^2} \end{equation}
If the smoothness indicator is large in a direction, meaning the tensor component has a large second derivative in that direction, the element should be hrefined. If the smoothness indicator is small, the element should be hcoarsened. A coarsing threshold of about a third of the refinement threshold seems to work well, but this will need more testing.
Note that it is not at all clear that a smoothness indicator based on the magnitude of second derivatives is useful in a DG context. Smooth functions with higherorder derivatives can be approximated just fine by higherorder DG elements without the need for hrefinement. The use of second derivatives to indicate the need for refinement originated in the finite element context with linear elements. Other smoothness indicators might prove more useful for DG elements, e.g. based on jumps or oscillations of the solution. We can also explore applying the troubledcell indicators (TCIs) used in hydrodynamic simulations as hrefinement indicators.
Specifically, this smoothness indicator is based on [125] (hence the name of the function), which is popular in the finite element community and also used in a DG context by [58], Eq. (34), and by [157], Eq. (15). We make several modifications:
In addition to the above modifications, we can consider approximating the second derivative using finite differences, as explored in the prototype https://github.com/sxscollaboration/dgcharm/blob/HpAmr/Evolution/HpAmr/LohnerRefiner.hpp. This would allow falling back to the normalization used by Löhner and might be cheaper to compute, but it requires an interpolation to the center and maybe also to the faces, depending on the desired stencil.
std::array< double, Dim > amr::Criteria::persson_smoothness_indicator  (  const DataVector &  tensor_component, 
const Mesh< Dim > &  mesh,  
size_t  num_highest_modes  
) 
Computes an anisotropic smoothness indicator based on the power in the highest modes.
This smoothness indicator is the L2 norm of the tensor component with the lowest N  M modes filtered out, where N is the number of grid points in the given dimension and M is num_highest_modes
.
This strategy is similar to the Persson troubledcell indicator (see evolution::dg::subcell::persson_tci
) with a few modifications:
double amr::Criteria::persson_smoothness_indicator  (  gsl::not_null< DataVector * >  filtered_component_buffer, 
const DataVector &  tensor_component,  
const Mesh< Dim > &  mesh,  
size_t  dimension,  
size_t  num_highest_modes  
) 
Computes an anisotropic smoothness indicator based on the power in the highest modes.
This smoothness indicator is the L2 norm of the tensor component with the lowest N  M modes filtered out, where N is the number of grid points in the given dimension and M is num_highest_modes
.
This strategy is similar to the Persson troubledcell indicator (see evolution::dg::subcell::persson_tci
) with a few modifications: