# Spherical and Temporal Meshes

#### Gilles Colling

#### 2026-06-15

## Spherical meshes

For global-scale species distribution models or climate fields, you need a mesh on the sphere. `tulpa_mesh_sphere()` generates one by recursive subdivision of an icosahedron.

    globe <- tulpa_mesh_sphere(subdivisions = 3)
    globe
    #> tulpa_mesh (sphere, radius = 1 ):
    #>   Vertices:   642
    #>   Triangles:  1280
    #>   Edges:      1920

Each subdivision level quadruples the triangle count:

Level | Vertices | Triangles
--- | --- | ---
0 | 12 | 20
1 | 42 | 80
2 | 162 | 320
3 | 642 | 1280
4 | 2562 | 5120

All vertices lie exactly on the sphere surface:

    radii <- sqrt(rowSums(globe$vertices^2))
    range(radii)
    #> [1] 1 1

### FEM on the sphere

The 3D FEM assembly uses the metric tensor on each triangle’s tangent plane:

    fem <- fem_matrices(globe, lumped = TRUE)

    # Total surface area should approximate 4*pi for unit sphere
    cat("Total area:", sum(fem$va), "\n")
    #> Total area: 12.50649
    cat("4*pi:      ", 4 * pi, "\n")
    #> 4*pi:       12.56637
    cat("Error:     ", abs(sum(fem$va) - 4 * pi) / (4 * pi) * 100, "%\n")
    #> Error:      0.476493 %

### Projection with lon/lat coordinates

Pass observation coordinates as (longitude, latitude) in degrees:

    obs <- cbind(lon = c(0, 90, -45, 170), lat = c(0, 45, -30, 60))
    fem <- fem_matrices(globe, obs_coords = obs)
    dim(fem$A)
    #> [1]   4 642
    range(Matrix::rowSums(fem$A))  # row sums = 1
    #> [1] 1 1

### Earth-radius meshes

    earth <- tulpa_mesh_sphere(subdivisions = 3, radius = 6371)
    sqrt(sum(earth$vertices[1, ]^2))  # 6371 km
    #> [1] 6371

### Euler characteristic

A closed sphere satisfies V - E + T = 2:

    globe$n_vertices - globe$n_edges + globe$n_triangles
    #> [1] 2

## 1D temporal meshes

For spatio-temporal SPDE models, the temporal dimension needs its own mesh. `tulpa_mesh_1d()` produces knots with tridiagonal FEM matrices that combine with the spatial mesh via Kronecker products.

    # Monthly observations over 5 years
    times <- seq(2020, 2025, by = 1/12)
    m1d <- tulpa_mesh_1d(times)
    m1d
    #> tulpa_mesh_1d:
    #>   Knots:     67
    #>   Range:    [2019.75, 2025.25]
    #>   Intervals: 66

The extension knots (controlled by `n_extend`) prevent boundary effects:

    range(times)       # data range
    #> [1] 2020 2025
    range(m1d$knots)   # mesh range (extended)
    #> [1] 2019.75 2025.25

### 1D FEM properties

Mass and stiffness matrices are tridiagonal and sparse:

    # No extension for cleaner demonstration
    m <- tulpa_mesh_1d(seq(0, 1, by = 0.1), n_extend = 0)

    # Symmetric, positive definite mass
    Matrix::isSymmetric(m$C)
    #> [1] TRUE
    all(Matrix::diag(m$C) > 0)
    #> [1] TRUE

    # Stiffness row sums = 0
    max(abs(Matrix::rowSums(m$G)))
    #> [1] 1.776357e-15

    # Total mass = domain length
    sum(m$C)
    #> [1] 1

### Irregular time spacing

The 1D mesh handles irregular intervals naturally:

    # Denser sampling in summer, sparser in winter
    times_irr <- c(
      seq(2020, 2020.25, by = 1/52),      # weekly Jan-Mar
      seq(2020.25, 2020.75, by = 1/365),   # daily Apr-Sep
      seq(2020.75, 2021, by = 1/52)        # weekly Oct-Dec
    )
    m_irr <- tulpa_mesh_1d(times_irr, n_extend = 0)
    cat(m_irr$n, "knots from", length(times_irr), "unique time points\n")
    #> 210 knots from 211 unique time points

## Metric graph meshes

For SPDE models along roads, rivers, or coastlines, `tulpa_mesh_graph()` builds a 1D FEM mesh along network edges.

    # Simple river network: main channel + tributary
    edges <- list(
      cbind(x = seq(0, 10, by = 0.5), y = rep(0, 21)),     # main channel
      cbind(x = c(5, 5, 5, 5), y = c(0, 2, 4, 6))          # tributary
    )

    g <- tulpa_mesh_graph(edges)
    g
    #> tulpa_mesh_graph:
    #>   Vertices:  24
    #>   Segments:  23
    #>   Junctions: 1   Endpoints: 3

### Junction detection

Nodes where edges meet are automatically detected:

    cat("Junctions (degree > 2):", sum(g$degree > 2), "\n")
    #> Junctions (degree > 2): 1
    cat("Endpoints (degree = 1):", sum(g$degree == 1), "\n")
    #> Endpoints (degree = 1): 3

### FEM on graphs

The graph FEM matrices are structurally identical to the 1D case but assembled across the full network:

    Matrix::isSymmetric(g$C)
    #> [1] TRUE
    max(abs(Matrix::rowSums(g$G)))  # row sums ~ 0
    #> [1] 0

### Subdividing long edges

    g_fine <- tulpa_mesh_graph(edges, max_edge = 0.3)
    cat("Coarse:", g$n_vertices, "vertices\n")
    #> Coarse: 24 vertices
    cat("Fine:  ", g_fine$n_vertices, "vertices\n")
    #> Fine:   62 vertices

### sf LINESTRING input

    library(sf)
    #> Linking to GEOS 3.14.1, GDAL 3.12.1, PROJ 9.7.1; sf_use_s2() is TRUE
    line1 <- st_linestring(cbind(c(0, 5, 10), c(0, 3, 0)))
    line2 <- st_linestring(cbind(c(5, 5), c(3, 8)))
    g_sf <- tulpa_mesh_graph(st_sfc(line1, line2), max_edge = 1)
    g_sf
    #> tulpa_mesh_graph:
    #>   Vertices:  18
    #>   Segments:  17
    #>   Junctions: 1   Endpoints: 3

## Non-stationary FEM

For fields where the range or variance changes across space, `fem_matrices_nonstationary()` computes weighted FEM matrices in C++:

    set.seed(42)
    mesh <- tulpa_mesh(cbind(runif(50), runif(50)), max_edge = 0.15)
    n <- mesh$n_vertices

    # Range decreases from left to right
    kappa <- sqrt(8) / (2 - mesh$vertices[, 1])  # shorter range on the right
    tau <- rep(1, n)

    ns <- fem_matrices_nonstationary(mesh, kappa, tau)
    names(ns)
    #> [1] "Ck"     "Gk"     "Ct"     "C"      "G"      "C0"     "n_mesh"

With constant kappa/tau, the weighted matrices are simply scaled versions of the standard ones:

    ns_const <- fem_matrices_nonstationary(mesh, rep(2, n), rep(3, n))
    fem <- fem_matrices(mesh)

    max(abs(ns_const$Ck - 4 * fem$C))  # kappa^2 = 4
    #> [1] 0
    max(abs(ns_const$Ct - 9 * fem$C))  # tau^2 = 9
    #> [1] 1.387779e-17

## P2 quadratic elements

For higher accuracy with fewer mesh nodes, use 6-node quadratic elements:

    set.seed(42)
    mesh <- tulpa_mesh(cbind(runif(30), runif(30)))
    p2 <- fem_matrices_p2(mesh)

    cat("P1 nodes:", mesh$n_vertices, "\n")
    #> P1 nodes: 40
    cat("P2 nodes:", p2$n_mesh, "(", p2$n_vertices, "vertices +",
        p2$n_midpoints, "midpoints)\n")
    #> P2 nodes: 147 ( 40 vertices + 107 midpoints)

The total area is preserved:

    fem_p1 <- fem_matrices(mesh)
    cat("P1 total area:", sum(fem_p1$C), "\n")
    #> P1 total area: 0.9660631
    cat("P2 total area:", sum(p2$C), "\n")
    #> P2 total area: 0.9660631

## Parallel FEM assembly

For large meshes (>50K triangles), enable parallel assembly:

    set.seed(42)
    mesh_large <- tulpa_mesh(cbind(runif(500), runif(500)), max_edge = 0.03)
    cat(mesh_large$n_triangles, "triangles\n")
    #> 3726 triangles

    fem_seq <- fem_matrices(mesh_large, parallel = FALSE)
    fem_par <- fem_matrices(mesh_large, parallel = TRUE)

    # Results are identical
    max(abs(fem_seq$C - fem_par$C))
    #> [1] 4.336809e-19