# DMPlex: Unstructured Grids in PETSc#

This chapter introduces the DMPLEX subclass of DM, which allows the user to handle unstructured grids using the generic DM interface for hierarchy and multi-physics. DMPlex was created to remedy a huge problem in all current PDE simulation codes, namely that the discretization was so closely tied to the data layout and solver that switching discretizations in the same code was not possible. Not only does this preclude the kind of comparison that is necessary for scientific investigation, but it makes library (as opposed to monolithic application) development impossible.

## Representing Unstructured Grids#

The main advantage of DMPlex in representing topology is that it treats all the different pieces of a mesh, e.g. cells, faces, edges, and vertices, in exactly the same way. This allows the interface to be very small and simple, while remaining flexible and general. This also allows “dimension independent programming”, which means that the same algorithm can be used unchanged for meshes of different shapes and dimensions.

All pieces of the mesh (vertices, edges, faces, and cells) are treated as points, which are identified by PetscInts. A mesh is built by relating points to other points, in particular specifying a “covering” relation among the points. For example, an edge is defined by being covered by two vertices, and a triangle can be defined by being covered by three edges (or even by three vertices). In fact, this structure has been known for a long time. It is a Hasse Diagram, which is a Directed Acyclic Graph (DAG) representing a cell complex using the covering relation. The graph edges represent the relation, which also encodes a partially ordered set (poset).

For example, we can encode the doublet mesh as in Fig. 8,

which can also be represented as the DAG in Fig. 9.

To use the PETSc API, we consecutively number the mesh pieces. The PETSc convention in 3 dimensions is to number first cells, then vertices, then faces, and then edges. In 2 dimensions the convention is to number faces, vertices, and then edges. In terms of the labels in Fig. 8, these numberings are

$f_0 \mapsto \mathtt{0}, f_1 \mapsto \mathtt{1}, \quad v_0 \mapsto \mathtt{2}, v_1 \mapsto \mathtt{3}, v_2 \mapsto \mathtt{4}, v_3 \mapsto \mathtt{5}, \quad e_0 \mapsto \mathtt{6}, e_1 \mapsto \mathtt{7}, e_2 \mapsto \mathtt{8}, e_3 \mapsto \mathtt{9}, e_4 \mapsto \mathtt{10}$

First, we declare the set of points present in a mesh,

DMPlexSetChart(dm, 0, 11);


Note that a chart here corresponds to a semi-closed interval (e.g $$[0,11) = \{0,1,\ldots,10\}$$) specifying the range of indices we’d like to use to define points on the current rank. We then define the covering relation, which we call the cone, which are also the in-edges in the DAG. In order to preallocate correctly, we first setup sizes,

DMPlexSetConeSize(dm, point, number of points that cover the point);
DMPlexSetConeSize(dm, 0, 3);
DMPlexSetConeSize(dm, 1, 3);
DMPlexSetConeSize(dm, 6, 2);
DMPlexSetConeSize(dm, 7, 2);
DMPlexSetConeSize(dm, 8, 2);
DMPlexSetConeSize(dm, 9, 2);
DMPlexSetConeSize(dm, 10, 2);
DMSetUp(dm);


and then point values,

DMPlexSetCone(dm, point, [points that cover the point]);
DMPlexSetCone(dm, 0, [6, 7, 8]);
DMPlexSetCone(dm, 1, [7, 9, 10]);
DMPlexSetCone(dm, 6, [2, 3]);
DMPlexSetCone(dm, 7, [3, 4]);
DMPlexSetCone(dm, 8, [4, 2]);
DMPlexSetCone(dm, 9, [4, 5]);
DMPlexSetCone(dm, 10, [5, 3]);


There is also an API for the dual relation, using DMPlexSetSupportSize() and DMPlexSetSupport(), but this can be calculated automatically by calling

DMPlexSymmetrize(dm);


The “symmetrization” in the sense of the DAG. Each point knows its covering (cone) and each point knows what it covers (support).

In order to support efficient queries, we construct fast search structures and indices for the different types of points using

DMPlexStratify(dm);


## Data on Unstructured Grids (PetscSection)#

The strongest links between solvers and discretizations are

• the layout of data over the mesh,

• problem partitioning, and

• ordering of unknowns.

To enable modularity, we encode the operations above in simple data structures that can be understood by the linear algebra engine in PETSc without any reference to the mesh (topology) or discretization (analysis).

### Data Layout by Hand#

Data are associated with a mesh using the PetscSection object.

A PetscSection, associates a set of degrees of freedom (dof), (a small space $$\{e_k\} 0 < k < d_p$$), with every point. The number of dof and their meaning may be different for different points. For example, the dof on a cell point may represent pressure while a dof on a face point may represent velocity. A reminder that though points must be contiguously numbered, they can be in any range $$[\mathrm{pStart}, \mathrm{pEnd})$$. A PetscSection may be thought of as defining a two dimensional array indexed by point in the outer dimension with a variable length inner dimension indexed by the dof at that point, $$v[pStart <= point < pEnd][0 <= dof <d_p]$$ 1.

The sequence for setting up any PetscSection is the following:

1. Specify the range of points, or chart,

2. Specify the number of dofs per point, and

3. Set up the PetscSection.

For example, using the mesh from Fig. 8, we can lay out data for a continuous Galerkin $$P_3$$ finite element method,

PetscInt pStart, pEnd, cStart, cEnd, c, vStart, vEnd, v, eStart, eEnd, e;

DMPlexGetChart(dm, &pStart, &pEnd);
DMPlexGetHeightStratum(dm, 0, &cStart, &cEnd);   // cells
DMPlexGetHeightStratum(dm, 1, &eStart, &eEnd);   // edges
DMPlexGetHeightStratum(dm, 2, &vStart, &vEnd);   // vertices, equivalent to DMPlexGetDepthStratum(dm, 0, &vStart, &vEnd);
PetscSectionSetChart(s, pStart, pEnd);
for(c = cStart; c < cEnd; ++c)
PetscSectionSetDof(s, c, 1);
for(v = vStart; v < vEnd; ++v)
PetscSectionSetDof(s, v, 1);
for(e = eStart; e < eEnd; ++e)
PetscSectionSetDof(s, e, 2); // two dof on each edge
PetscSectionSetUp(s);


DMPlexGetHeightStratum() returns all the points of the requested height in the DAG. Since this problem is in two dimensions the edges are at height 1 and the vertices at height 2 (the cells are always at height 0). One can also use DMPlexGetDepthStratum() to use the depth in the DAG to select the points. DMPlexGetDepth(dm,&depth) returns the depth of the DAG, hence DMPlexGetDepthStratum(dm,depth-1-h,) returns the same values as DMPlexGetHeightStratum(dm,h,).

For $$P_3$$ elements there is one degree of freedom at each vertex, 2 along each edge (resulting in a total of 4 degrees of freedom along each edge including the vertices, thus being able to reproduce a cubic function) and 1 degree of freedom within the cell (the bubble function which is zero along all edges).

Now a PETSc local vector can be created manually using this layout,

PetscSectionGetStorageSize(s, &n);
VecSetSizes(localVec, n, PETSC_DETERMINE);
VecSetFromOptions(localVec);


though it is usually easier to use the DM directly, which also provides global vectors,

DMSetLocalSection(dm, s);
DMGetLocalVector(dm, &localVec);
DMGetGlobalVector(dm, &globalVec);


A global vector is missing both the shared dofs which are not owned by this process, as well as constrained dofs. These constraints represent essential (Dirichlet) boundary conditions. They are dofs that have a given fixed value, so they are present in local vectors for assembly purposes, but absent from global vectors since they are never solved for during algebraic solves.

We can indicate constraints in a local section using PetscSectionSetConstraintDof(), to set the number of constrained dofs for a given point, and PetscSectionSetConstraintIndices() which indicates which dofs on the given point are constrained. Once we have this information, a global section can be created using PetscSectionCreateGlobalSection(), and this is done automatically by the DM. A global section returns $$-(dof+1)$$ for the number of dofs on an unowned point, and $$-(off+1)$$ for its offset on the owning process. This can be used to create global vectors, just as the local section is used to create local vectors.

### Data Layout using PetscFE#

A DM can automatically create the local section if given a description of the discretization, for example using a PetscFE object. Below we create a PetscFE that can be configured from the command line. It is a single, scalar field, and is added to the DM using DMSetField(). When a local or global vector is requested, the DM builds the local and global sections automatically.

DMPlexIsSimplex(dm, &simplex);
PetscFECreateDefault(PETSC_COMM_SELF, dim, 1, simplex, NULL, -1, &fe);
DMSetField(dm, 0, NULL, (PetscObject) fe);
DMCreateDS(dm);


Here the call to DMSetField() declares the discretization will have one field with the integer label 0 that has one degree of freedom at each point on the DMPlex. To get the $$P_3$$ section above, we can either give the option -petscspace_degree 3, or call PetscFECreateLagrange() and set the degree directly.

### Partitioning and Ordering#

In the same way as MatPartitioning or MatGetOrdering(), give the results of a partitioning or ordering of a graph defined by a sparse matrix, PetscPartitionerDMPlexPartition or DMPlexPermute are encoded in an IS. However, the graph is not the adjacency graph of the matrix but the mesh itself. Once the mesh is partitioned and reordered, the data layout from a PetscSection can be used to automatically derive a problem partitioning/ordering.

### Influence of Variables on One Another#

The Jacobian of a problem represents the influence of some variable on other variables in the problem. Very often, this influence pattern is determined jointly by the computational mesh and discretization. DMCreateMatrix() must compute this pattern when it automatically creates the properly preallocated Jacobian matrix. In DMDA the influence pattern, or what we will call variable adjacency, depends only on the stencil since the topology is Cartesian and the discretization is implicitly finite difference.

In DMPlex, we allow the user to specify the adjacency topologically, while maintaining good defaults. The pattern is controlled by two flags. The first flag, useCone, indicates whether variables couple first to their boundary 2 and then to neighboring entities, or the reverse. For example, in finite elements, the variables couple to the set of neighboring cells containing the mesh point, and we set the flag to useCone = PETSC_FALSE. By constrast, in finite volumes, cell variables first couple to the cell boundary, and then to the neighbors, so we set the flag to useCone = PETSC_TRUE. The second flag, useClosure, indicates whether we consider the transitive closure of the neighbor relation above, or just a single level. For example, in finite elements, the entire boundary of any cell couples to the interior, and we set the flag to useClosure = PETSC_TRUE. By contrast, in most finite volume methods, cells couple only across faces, and not through vertices, so we set the flag to useClosure = PETSC_FALSE. However, the power of this method is its flexibility. If we wanted a finite volume method that coupled all cells around a vertex, we could easily prescribe that by changing to useClosure = PETSC_TRUE.

## Evaluating Residuals#

The evaluation of a residual or Jacobian, for most discretizations has the following general form:

• Traverse the mesh, picking out pieces (which in general overlap),

• Extract some values from the current solution vector, associated with this piece,

• Calculate some values for the piece, and

• Insert these values into the residual vector

DMPlex separates these different concerns by passing sets of points from mesh traversal routines to data extraction routines and back. In this way, the PetscSection which structures the data inside a Vec does not need to know anything about the mesh inside a DMPlex.

The most common mesh traversal is the transitive closure of a point, which is exactly the transitive closure of a point in the DAG using the covering relation. In other words, the transitive closure consists of all points that cover the given point (generally a cell) plus all points that cover those points, etc. So in 2d the transitive closure for a cell consists of edges and vertices while in 3d it consists of faces, edges, and vertices. Note that this closure can be calculated orienting the arrows in either direction. For example, in a finite element calculation, we calculate an integral over each element, and then sum up the contributions to the basis function coefficients. The closure of the element can be expressed discretely as the transitive closure of the element point in the mesh DAG, where each point also has an orientation. Then we can retrieve the data using PetscSection methods,

PetscScalar *a;
PetscInt     numPoints, *points = NULL, p;

DMPlexGetTransitiveClosure(dm,cell,PETSC_TRUE,&numPoints,&points);
for (p = 0; p <= numPoints*2; p += 2) {
PetscInt dof, off, d;

PetscSectionGetDof(section, points[p], &dof);
PetscSectionGetOffset(section, points[p], &off);
for (d = 0; d <= dof; ++d) {
myfunc(a[off+d]);
}
}
DMPlexRestoreTransitiveClosure(dm, cell, PETSC_TRUE, &numPoints, &points);


This operation is so common that we have built a convenience method around it which returns the values in a contiguous array, correctly taking into account the orientations of various mesh points:

const PetscScalar *values;
PetscInt           csize;

DMPlexVecGetClosure(dm, section, u, cell, &csize, &values);
// Do integral in quadrature loop putting the result into r[]
DMPlexVecRestoreClosure(dm, section, u, cell, &csize, &values);
DMPlexVecSetClosure(dm, section, residual, cell, &r, ADD_VALUES);


A simple example of this kind of calculation is in DMPlexComputeL2Diff_Plex() (source). Note that there is no restriction on the type of cell or dimension of the mesh in the code above, so it will work for polyhedral cells, hybrid meshes, and meshes of any dimension, without change. We can also reverse the covering relation, so that the code works for finite volume methods where we want the data from neighboring cells for each face:

PetscScalar *a;
PetscInt     points[2*2], numPoints, p, dofA, offA, dofB, offB;

VecGetArray(u,  &a);
DMPlexGetTransitiveClosure(dm, cell, PETSC_FALSE, &numPoints, &points);
assert(numPoints == 2);
PetscSectionGetDof(section, points[0*2], &dofA);
PetscSectionGetDof(section, points[1*2], &dofB);
assert(dofA == dofB);
PetscSectionGetOffset(section, points[0*2], &offA);
PetscSectionGetOffset(section, points[1*2], &offB);
myfunc(a[offA], a[offB]);
VecRestoreArray(u, &a);


This kind of calculation is used in TS Tutorial ex11.

PETSc allows users to save/load DMPlexs representing meshes, PetscSections representing data layouts on the meshes, and Vecs defined on the data layouts to/from an HDF5 file in parallel, where one can use different number of processes for saving and for loading.

### Saving#

The simplest way to save DM data is to use options for configuration. This requires only the code

DMViewFromOptions(dm, NULL, "-dm_view");
VecViewFromOptions(vec, NULL, "-vec_view")


along with the command line options

\$ ./myprog -dm_view hdf5:myprog.h5 -vec_view hdf5:myprog.h5::append


Options prefixes can be used to separately control the saving and loading of various fields. However, the user can have finer grained control by explicitly creating the PETSc objects involved. To save data to “example.h5” file, we can first create a PetscViewer of type PETSCVIEWERHDF5 in FILE_MODE_WRITE mode as:

PetscViewer  viewer;

PetscViewerHDF5Open(PETSC_COMM_WORLD, "example.h5", FILE_MODE_WRITE, &viewer);


As dm is a DMPlex object representing a mesh, we first give it a mesh name, “plexA”, and save it as:

PetscObjectSetName((PetscObject)dm, "plexA");
PetscViewerPushFormat(viewer, PETSC_VIEWER_HDF5_PETSC);
DMView(dm, viewer);
PetscViewerPopFormat(viewer);


The DMView() call is shorthand for the following sequence

DMPlexTopologyView(dm, viewer);
DMPlexCoordinatesView(dm, viewer);
DMPlexLabelsView(dm, viewer);


If the mesh name is not explicitly set, the default name is used. In the above PETSC_VIEWER_HDF5_PETSC format was used to save the entire representation of the mesh. This format also saves global point numbers attached to the mesh points. In this example the set of all global point numbers is $$X = [0, 11)$$.

The data layout, s, needs to be wrapped in a DM object for it to be saved. Here, we create the wrapping DM, sdm, with DMClone(), give it a dm name, “dmA”, attach s to sdm, and save it as:

DMClone(dm, &sdm);
PetscObjectSetName((PetscObject)sdm, "dmA");
DMSetLocalSection(sdm, s);
DMPlexSectionView(dm, viewer, sdm);


If the dm name is not explicitly set, the default name is to be used. In the above, instead of using DMClone(), one could also create a new DMSHELL object to attach s to. The first argument of DMPlexSectionView() is a DMPLEX object that represents the mesh, and the third argument is a DM object that carries the data layout that we would like to save. They are, in general, two different objects, and the former carries a mesh name, while the latter carries a dm name. These names are used to construct a group structure in the HDF5 file. Note that the data layout points are associated with the mesh points, so each of them can also be tagged with a global point number in $$X$$; DMPlexSectionView() saves these tags along with the data layout itself, so that, when the mesh and the data layout are loaded separately later, one can associate the points in the former with those in the latter by comparing their global point numbers.

We now create a local vector assiciated with sdm, e.g., as:

Vec  vec;

DMGetLocalVector(sdm, &vec);


After setting values of vec, we name it “vecA” and save it as:

PetscObjectSetName((PetscObject)vec, "vecA");
DMPlexLocalVectorView(dm, viewer, sdm, vec);


A global vector can be saved in the exact same way with trivial changes.

After saving, we destroy the PetscViewer with:

PetscViewerDestroy(&viewer);


The output file “example.h5” now looks like the following:

HDF5 "example.h5" {
FILE_CONTENTS {
group      /
group      /topologies
group      /topologies/plexA
group      /topologies/plexA/dms
group      /topologies/plexA/dms/dmA
dataset    /topologies/plexA/dms/dmA/order
group      /topologies/plexA/dms/dmA/section
dataset    /topologies/plexA/dms/dmA/section/atlasDof
dataset    /topologies/plexA/dms/dmA/section/atlasOff
group      /topologies/plexA/dms/dmA/vecs
group      /topologies/plexA/dms/dmA/vecs/vecA
dataset    /topologies/plexA/dms/dmA/vecs/vecA/vecA
group      /topologies/plexA/topology
dataset    /topologies/plexA/topology/cells
dataset    /topologies/plexA/topology/cones
dataset    /topologies/plexA/topology/order
dataset    /topologies/plexA/topology/orientation
}
}


To load data from “example.h5” file, we create a PetscViewer of type PETSCVIEWERHDF5 in FILE_MODE_READ mode as:

PetscViewerHDF5Open(PETSC_COMM_WORLD, "example.h5", FILE_MODE_READ, &viewer);


We then create a DMPlex object, give it a mesh name, “plexA”, and load the mesh as:

DMCreate(PETSC_COMM_WORLD, &dm);
DMSetType(dm, DMPLEX);
PetscObjectSetName((PetscObject)dm, "plexA");
PetscViewerPushFormat(viewer, PETSC_VIEWER_HDF5_PETSC);
PetscViewerPopFormat(viewer);


where PETSC_VIEWER_HDF5_PETSC format was again used. The user can have more control by replace the single load call with

PetscSF  sfO;

DMCreate(PETSC_COMM_WORLD, &dm);
DMSetType(dm, DMPLEX);
PetscObjectSetName((PetscObject)dm, "plexA");
PetscViewerPushFormat(viewer, PETSC_VIEWER_HDF5_PETSC);
PetscViewerPopFormat(viewer);


The object returned by DMPlexTopologyLoad(), sfO, is a PetscSF that pushes forward $$X$$ to the loaded mesh, dm; this PetscSF is constructed with the global point number tags that we saved along with the mesh points.

As the DMPlex mesh just loaded might not have a desired distribution, it is common to redistribute the mesh for a better distribution using DMPlexDistribute(), e.g., as:

DM        distributedDM;
PetscInt  overlap = 1;
PetscSF   sfDist, sf;

DMPlexDistribute(dm, overlap, &sfDist, &distributedDM);
if (distributedDM) {
DMDestroy(&dm);
dm = distributedDM;
PetscObjectSetName((PetscObject)dm, "plexA");
}
PetscSFCompose(sfO, sfDist, &sf);
PetscSFDestroy(&sfO);
PetscSFDestroy(&sfDist);


Note that the new DMPlex does not automatically inherit the mesh name, so we need to name it “plexA” once again. sfDist is a PetscSF that pushes forward the loaded mesh to the redistributed mesh, so, composed with sfO, it makes the PetscSF that pushes forward $$X$$ directly to the redistributed mesh, which we call sf.

We then create a new DM, sdm, with DMClone(), give it a dm name, “dmA”, and load the on-disk data layout into sdm as:

PetscSF  globalDataSF, localDataSF;

DMClone(dm, &sdm);
PetscObjectSetName((PetscObject)sdm, "dmA");
DMPlexSectionLoad(dm, viewer, sdm, sf, &globalDataSF, &localDataSF);


where we could also create a new DMSHELL object instead of using DMClone(). Each point in the on-disk data layout being tagged with a global point number in $$X$$, DMPlexSectionLoad() internally constructs a PetscSF that pushes forward the on-disk data layout to $$X$$. Composing this with sf, DMPlexSectionLoad() internally constructs another PetscSF that pushes forward the on-disk data layout directly to the redistributed mesh. It then reconstructs the data layout s on the redistributed mesh and attaches it to sdm. The objects returned by this function, globalDataSF and localDataSF, are PetscSFs that can be used to migrate the on-disk vector data into local and global Vecs defined on sdm.

We now create a local vector assiciated with sdm, e.g., as:

Vec  vec;

DMGetLocalVector(sdm, &vec);


We then name vec “vecA” and load the on-disk vector into vec as:

PetscObjectSetName((PetscObject)vec, "vecA");


where localDataSF knows how to migrate the on-disk vector data into a local Vec defined on sdm. The on-disk vector can be loaded into a global vector associated with sdm in the exact same way with trivial changes.

After loading, we destroy the PetscViewer with:

PetscViewerDestroy(&viewer);


The above infrastructure works seamlessly in distributed-memory parallel settings, in which one can even use different number of processes for saving and for loading; a more comprehensive example is found in DMPlex Tutorial ex12.

DMPlex supports mesh adaptation using the Riemannian metric framework. The idea is to use a Riemannian metric space within the mesher. The metric space dictates how mesh resolution should be distributed across the domain. Using this information, the remesher transforms the mesh such that it is a unit mesh when viewed in the metric space. That is, the image of each of its elements under the mapping from Euclidean space into the metric space has edges of unit length.

One of the main advantages of metric-based mesh adaptation is that it allows for fully anisotropic remeshing. That is, it provides a means of controlling the shape and orientation of elements in the adapted mesh, as well as their size. This can be particularly useful for advection-dominated and directionally-dependent problems.

See [Ala10] for further details on metric-based anisotropic mesh adaptation.

The two main ingredients for metric-based mesh adaptation are an input mesh (i.e. the DMPlex) and a Riemannian metric. The implementation in PETSc assumes that the metric is piecewise linear and continuous across elemental boundaries. Such an object can be created using the routine

DMPlexMetricCreate(DM dm, PetscInt field, Vec *metric);


A metric must be symmetric positive-definite, so that distances may be properly defined. This can be checked using

DMPlexMetricEnforceSPD(DM dm, Vec metricIn, PetscBool restrictSizes, PetscBool restrictAnisotropy, Vec metricOut, Vec determinant);


This routine may also be used to enforce minimum and maximum tolerated metric magnitudes (i.e. cell sizes), as well as maximum anisotropy. These quantities can be specified using

DMPlexMetricSetMinimumMagnitude(DM dm, PetscReal h_min);
DMPlexMetricSetMaximumMagnitude(DM dm, PetscReal h_max);
DMPlexMetricSetMaximumAnisotropy(DM dm, PetscReal a_max);


or the command line arguments

-dm_plex_metric_h_min <h_min>
-dm_plex_metric_h_max <h_max>
-dm_plex_metric_a_max <a_max>


One simple way to combine two metrics is by simply averaging them entry-by-entry. Another is to intersect them, which amounts to choosing the greatest level of refinement in each direction. These operations are available in PETSc through the routines

DMPlexMetricAverage(DM dm, PetscInt numMetrics, PetscReal weights[], Vec metrics[], Vec metricAvg);
DMPlexMetricIntersection(DM dm, PetscInt numMetrics, Vec metrics[], Vec metricInt);


However, before combining metrics, it is important that they are scaled in the same way. Scaling also allows the user to control the number of vertices in the adapted mesh (in an approximate sense). This is achieved using the $$L^p$$ normalization framework, with the routine

DMPlexMetricNormalize(DM dm, Vec metricIn, PetscBool restrictSizes, PetscBool restrictAnisotropy, Vec metricOut, Vec determinant);


There are two important parameters for the normalization: the normalization order $$p$$ and the target metric complexity, which is analogous to the vertex count. They are controlled using

DMPlexMetricSetNormalizationOrder(DM dm, PetscReal p);
DMPlexMetricSetTargetComplexity(DM dm, PetscReal target);


or the command line arguments

-dm_plex_metric_p <p>
-dm_plex_metric_target_complexity <target>


Two different metric-based mesh adaptation tools are available in PETSc:

Mmg is a serial package, whereas ParMmg is the MPI version. Note that surface meshing is not currently supported and that ParMmg works only in three dimensions. Mmg can be used for both two and three dimensional problems. Pragmatic, Mmg and ParMmg may be specified by the command line arguments

-dm_adaptor pragmatic


Once a metric has been constructed, it can be used to perform metric-based mesh adaptation using the routine

DMAdaptMetric(DM dm, Vec metric, DMLabel bdLabel, DMLabel rgLabel, DM dmAdapt);


where bdLabel and rgLabel are boundary and interior tags to be preserved under adaptation, respectively.

Footnotes

1

A PetscSection can be thought of as a generalization of PetscLayout, in the same way that a fiber bundle is a generalization of the normal Euclidean basis used in linear algebra. With PetscLayout, we associate a unit vector ($$e_i$$) with every point in the space, and just divide up points between processes.

2

The boundary of a cell is its faces, the boundary of a face is its edges and the boundary of an edge is the two vertices.

Ala10

Frédéric Alauzet. Metric-based anisotropic mesh adaptation. https://pages.saclay.inria.fr/frederic.alauzet/cours/cea2010_V3.pdf, 2010.