Merge pull request #6 from scaomath/0.2.4-dev

Added multigrid solvers for pressure projection and tests

Author: scaomath

torch_cfd/advection.py

@@ -282,6 +282,9 @@ def forward(self, cs: GridVariableVector, v: GridVariableVector) -> GridVariable
         flux = GridVariableVector(tuple(c * u for c, u in zip(cs, v)))
 
         # wrap flux with boundary conditions to flux if not periodic
+        # flux = GridVariableVector(
+        #         tuple(bc.impose_bc(f) for f, bc in zip(flux, self.flux_bcs))
+        #     )
         flux = GridVariableVector(tuple(GridVariable(f.data, offset, f.grid, bc) for f, offset, bc in zip(flux, self.offsets, self.flux_bcs)))
 
 

torch_cfd/boundaries.py

@@ -384,7 +384,7 @@ def pad_and_impose_bc(
                     )
         return GridVariable(u.data, u.offset, u.grid, self)
 
-    def impose_bc(self, u: GridVariable, mode: str="") -> GridVariable:
+    def impose_bc(self, u: GridVariable, mode: str = "") -> GridVariable:
         """Returns GridVariable with correct boundary condition.
 
         Some grid points of GridVariable might coincide with boundary. This ensures
@@ -435,12 +435,32 @@ def is_bc_periodic_boundary_conditions(bc: BoundaryConditions, dim: int) -> bool
         )
     return True
 
+def is_bc_all_periodic_boundary_conditions(bc: BoundaryConditions) -> bool:
+    """Returns true if scalar has periodic bc along all axes."""
+    for dim in range(bc.ndim):
+        if not is_bc_periodic_boundary_conditions(bc, dim):
+            return False
+    return True
+
 
 def is_periodic_boundary_conditions(c: GridVariable, dim: int) -> bool:
     """Returns true if scalar has periodic bc along axis."""
     return is_bc_periodic_boundary_conditions(c.bc, dim)
 
 
+def is_bc_pure_neumann_boundary_conditions(bc: BoundaryConditions) -> bool:
+    """Returns true if scalar has pure Neumann bc along all axes."""
+    for dim in range(bc.ndim):
+        if bc.types[dim][0] != BCType.NEUMANN or bc.types[dim][1] != BCType.NEUMANN:
+            return False
+    return True
+
+
+def is_pure_neumann_boundary_conditions(c: GridVariable) -> bool:
+    """Returns true if scalar has pure Neumann bc along all axes."""
+    return is_bc_pure_neumann_boundary_conditions(c.bc)
+
+
 # Convenience utilities to ease updating of BoundaryConditions implementation
 def periodic_boundary_conditions(ndim: int) -> BoundaryConditions:
     """Returns periodic BCs for a variable with `ndim` spatial dimension."""

torch_cfd/finite_differences.py

@@ -42,7 +42,7 @@
 import torch
 from torch_cfd import boundaries, grids
 
-ArrayVector = Sequence[torch.Tensor]
+ArrayVector = List[torch.Tensor]
 GridVariable = grids.GridVariable
 GridTensor = grids.GridTensor
 GridVariableVector = Union[grids.GridVariableVector, Sequence[grids.GridVariable]]
@@ -159,20 +159,22 @@ def set_laplacian_matrix(
     grid: grids.Grid,
     bc: boundaries.BoundaryConditions,
     device: Optional[torch.device] = None,
+    dtype: torch.dtype = torch.float32,
 ) -> ArrayVector:
     """Initialize the Laplacian operators."""
 
     offset = grid.cell_center
-    return laplacian_matrix_w_boundaries(grid, offset=offset, bc=bc, device=device)
+    return laplacian_matrix_w_boundaries(grid, offset=offset, bc=bc, device=device, dtype=dtype)
 
 
-def laplacian_matrix(n: int, step: float, sparse: bool = False) -> torch.Tensor:
+def laplacian_matrix(n: int, step: float, sparse: bool = False, dtype=torch.float32) -> torch.Tensor:
     """
     Create 1D Laplacian operator matrix, with periodic BC.
-    modified the scipy.linalg.circulant implementation to native torch
+    The matrix is a tri-diagonal matrix with [1, -2, 1]/h**2
+    Modified the scipy.linalg.circulant implementation to native torch
     """
     if sparse:
-        values = torch.tensor([1.0, -2.0, 1.0]) / step**2
+        values = torch.tensor([1.0, -2.0, 1.0], dtype=dtype) / step**2
         idx_row = torch.arange(n).repeat(3)
         idx_col = torch.cat(
             [
@@ -188,33 +190,45 @@ def laplacian_matrix(n: int, step: float, sparse: bool = False) -> torch.Tensor:
         )
         return torch.sparse_coo_tensor(indices, data, size=(n, n))
     else:
-        column = torch.zeros(n)
+        column = torch.zeros(n, dtype=dtype)
         column[0] = -2 / step**2
         column[1] = column[-1] = 1 / step**2
         idx = (n - torch.arange(n)[None].T + torch.arange(n)[None]) % n
         return torch.gather(column[None, ...].expand(n, -1), 1, idx)
 
 
 def _laplacian_boundary_dirichlet_cell_centered(
-    laplacians: ArrayVector, grid: grids.Grid, axis: int, side: str
+    laplacians: ArrayVector, grid: grids.Grid, dim: int, side: str
 ) -> None:
     """Converts 1d laplacian matrix to satisfy dirichlet homogeneous bc.
 
     laplacians[i] contains a 3 point stencil matrix L that approximates
     d^2/dx_i^2.
     For detailed documentation on laplacians input type see
-    array_utils.laplacian_matrix.
-    The default return of array_utils.laplacian_matrix makes a matrix for
-    periodic boundary. For dirichlet boundary, the correct equation is
-    L(u_interior) = rhs_interior and BL_boundary = u_fixed_boundary. So
+    fdm.laplacian_matrix.
+    The default return of fdm.laplacian_matrix makes a matrix for
+    periodic boundary. For (homogeneous) dirichlet boundary, the correct equation is 
+        L(u_interior) = rhs_interior
+        BL_boundary = u_fixed_boundary. 
+    So
     laplacian_boundary_dirichlet restricts the matrix L to
-    interior points only.
+    interior points only. 
+
+    Denote the node in the 3-pt stencil as 
+    u[ghost], u[boundary], u[interior] = u[0], u[1], u[2].
+    The original stencil on the boundary is
+    [1, -2, 1] * [u[0], u[1], u[2]] = u[0] - 2*u[1] + u[2]
+    In the homogeneous Dirichlet bc case if the offset
+    is 0.5 away from the wall, the ghost cell value u[0] = -u[1]. So the
+    3 point stencil [1 -2 1] * [u[0] u[1] u[2]] = -3 u[1] + u[2].
+    The original diagonal of Laplacian Lap[0, 0] is -2/h**2, we need -3/h**2, 
+    thus 1/h**2 is subtracted from the diagonal, and the ghost cell dof is set to zero (Lap[0, -1])
 
     This function assumes RHS has cell-centered offset.
     Args:
       laplacians: list of 1d laplacians
       grid: grid object
-      axis: axis along which to impose dirichlet bc.
+      dim: axis along which to impose dirichlet bc.
       side: lower or upper side to assign boundary to.
 
     Returns:
@@ -223,52 +237,50 @@ def _laplacian_boundary_dirichlet_cell_centered(
     TODO:
     [ ]: this function is not implemented in the original Jax-CFD code.
     """
-    # This function assumes homogeneous boundary, in which case if the offset
-    # is 0.5 away from the wall, the ghost cell value u[0] = -u[1]. So the
-    # 3 point stencil [1 -2 1] * [u[0] u[1] u[2]] = -3 u[1] + u[2].
+
     if side == "lower":
-        laplacians[axis][0, 0] = laplacians[axis][0, 0] - 1 / grid.step[axis] ** 2
+        laplacians[dim][0, 0] = laplacians[dim][0, 0] - 1 / grid.step[dim] ** 2
     else:
-        laplacians[axis][-1, -1] = laplacians[axis][-1, -1] - 1 / grid.step[axis] ** 2
+        laplacians[dim][-1, -1] = laplacians[dim][-1, -1] - 1 / grid.step[dim] ** 2
     # deletes corner dependencies on the "looped-around" part.
     # this should be done irrespective of which side, since one boundary cannot
     # be periodic while the other is.
-    laplacians[axis][0, -1] = 0.0
-    laplacians[axis][-1, 0] = 0.0
-    return laplacians
+    laplacians[dim][0, -1] = 0.0
+    laplacians[dim][-1, 0] = 0.0
+    return
 
 
 def _laplacian_boundary_neumann_cell_centered(
-    laplacians: List[Any], grid: grids.Grid, axis: int, side: str
+    laplacians: ArrayVector, grid: grids.Grid, dim: int, side: str
 ) -> None:
     """Converts 1d laplacian matrix to satisfy neumann homogeneous bc.
 
     This function assumes the RHS will have a cell-centered offset.
     Neumann boundaries are not defined for edge-aligned offsets elsewhere in the
-    code.
+    code. For homogeneous Neumann BC (du/dn = 0), the ghost cell should equal the interior cell: u[ghost] = u[1]. The stencil becomes:
+    [1, -2, 1] * [u[1], u[1], u[2]] = u[1] - 2*u[1] + u[2] = -u[1] + u[2]
+    The original diagonal of Laplacian Lap[0, 0] is -2/h**2, we need -1/h**2,
+    thus 1/h**2 is added to the diagonal, and the ghost cell dof is set to zero (Lap[0, -1]).
 
     Args:
       laplacians: list of 1d laplacians
       grid: grid object
-      axis: axis along which to impose dirichlet bc.
+      dim: axis along which to impose dirichlet bc.
       side: which boundary side to convert to neumann homogeneous bc.
 
     Returns:
       updated list of 1d laplacians.
-
-    TODO
-    [ ]: this function is not implemented in the original Jax-CFD code.
     """
     if side == "lower":
-        laplacians[axis][0, 0] = laplacians[axis][0, 0] + 1 / grid.step[axis] ** 2
+        laplacians[dim][0, 0] = laplacians[dim][0, 0] + 1 / grid.step[dim] ** 2
     else:
-        laplacians[axis][-1, -1] = laplacians[axis][-1, -1] + 1 / grid.step[axis] ** 2
+        laplacians[dim][-1, -1] = laplacians[dim][-1, -1] + 1 / grid.step[dim] ** 2
     # deletes corner dependencies on the "looped-around" part.
     # this should be done irrespective of which side, since one boundary cannot
     # be periodic while the other is.
-    laplacians[axis][0, -1] = 0.0
-    laplacians[axis][-1, 0] = 0.0
-    return laplacians
+    laplacians[dim][0, -1] = 0.0
+    laplacians[dim][-1, 0] = 0.0
+    return
 
 
 def laplacian_matrix_w_boundaries(
@@ -277,6 +289,7 @@ def laplacian_matrix_w_boundaries(
     bc: grids.BoundaryConditions,
     laplacians: Optional[ArrayVector] = None,
     device: Optional[torch.device] = None,
+    dtype: torch.dtype = torch.float32,
     sparse: bool = False,
 ) -> ArrayVector:
     """Returns 1d laplacians that satisfy boundary conditions bc on grid.
@@ -323,11 +336,13 @@ def laplacian_matrix_w_boundaries(
                 raise NotImplementedError(
                     "edge-aligned Neumann boundaries are not implemented."
                 )
-    return list(lap.to(device) for lap in laplacians) if device else laplacians
+    return list(lap.to(dtype).to(device) for lap in laplacians)
 
 
 def _linear_along_axis(c: GridVariable, offset: float, dim: int) -> GridVariable:
-    """Linear interpolation of `c` to `offset` along a single specified `axis`."""
+    """Linear interpolation of `c` to `offset` along a single specified `axis`.
+    dim here is >= 0, the negative indexing for batched implementation is handled by grids.shift.
+    """
     offset_delta = offset - c.offset[dim]
 
     # If offsets are the same, `c` is unchanged.
@@ -383,8 +398,8 @@ def linear(
             f"got {c.offset} and {offset}."
         )
     interpolated = c
-    for a, o in enumerate(offset):
-        interpolated = _linear_along_axis(interpolated, offset=o, dim=a)
+    for dim, o in enumerate(offset):
+        interpolated = _linear_along_axis(interpolated, offset=o, dim=dim)
     return interpolated
 
 
@@ -405,15 +420,15 @@ def gradient_tensor(v):
     if not isinstance(v, GridVariable):
         return GridTensor(torch.stack([gradient_tensor(u) for u in v], dim=-1))
     grad = []
-    for axis in range(v.grid.ndim):
-        offset = v.offset[axis]
+    for dim in range(-v.grid.ndim, 0):
+        offset = v.offset[dim]
         if offset == 0:
-            derivative = forward_difference(v, axis)
+            derivative = forward_difference(v, dim)
         elif offset == 1:
-            derivative = backward_difference(v, axis)
+            derivative = backward_difference(v, dim)
         elif offset == 0.5:
             v_centered = linear(v, v.grid.cell_center)
-            derivative = central_difference(v_centered, axis)
+            derivative = central_difference(v_centered, dim)
         else:
             raise ValueError(f"expected offset values in {{0, 0.5, 1}}, got {offset}")
         grad.append(derivative)
@@ -427,4 +442,4 @@ def curl_2d(v: GridVariableVector) -> GridVariable:
     grid = grids.consistent_grid_arrays(*v)
     if grid.ndim != 2:
         raise ValueError(f"Grid dimensionality is not 2: {grid.ndim}")
-    return forward_difference(v[1], dim=0) - forward_difference(v[0], dim=1)
+    return forward_difference(v[1], dim=-2) - forward_difference(v[0], dim=-1)

torch_cfd/forcings.py

@@ -25,6 +25,7 @@
 
 Grid = grids.Grid
 GridVariable = grids.GridVariable
+GridVariableVector = grids.GridVariableVector
 
 
 def forcing_eval(eval_func):
@@ -79,7 +80,7 @@ class ForcingFn(nn.Module):
     def __init__(
         self,
         grid: Grid,
-        scale: float = 1,
+        scale: float = 1.0,
         wave_number: int = 1,
         diam: float = 1.0,
         swap_xy: bool = False,
@@ -100,20 +101,20 @@ def __init__(
 
     @forcing_eval
     def velocity_eval(
-        grid: Grid, velocity: Optional[Tuple[torch.Tensor, torch.Tensor]]
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        self, grid: Grid, velocity: Optional[Tuple[GridVariable, GridVariable]]
+    ) -> GridVariableVector:
         raise NotImplementedError
 
     @forcing_eval
-    def vorticity_eval(grid: Grid, vorticity: Optional[torch.Tensor]) -> torch.Tensor:
+    def vorticity_eval(self, grid: Grid, vorticity: Optional[torch.Tensor]) -> GridVariable:
         raise NotImplementedError
 
     def forward(
         self,
         grid: Optional[Union[Grid, Tuple[Grid, Grid]]] = None,
         velocity: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
         vorticity: Optional[torch.Tensor] = None,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
+    ) -> Union[GridVariable, GridVariableVector]:
         if not self.vorticity:
             return self.velocity_eval(grid, velocity)
         else:
@@ -166,7 +167,7 @@ def velocity_eval(
         self,
         grid: Optional[Grid],
         velocity: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
+    ) -> GridVariableVector:
         offsets = self.offsets
         grid = self.grid if grid is None else grid
         domain_factor = 2 * torch.pi / self.diam
@@ -187,13 +188,13 @@ def velocity_eval(
                 grid,
             )
             v = GridVariable(torch.zeros_like(u.data), (1 / 2, 1), grid)
-        return tuple((u, v))
+        return GridVariableVector(tuple((u, v)))
 
     def vorticity_eval(
         self,
         grid: Optional[Grid],
         vorticity: Optional[torch.Tensor] = None,
-    ) -> torch.Tensor:
+    ) -> GridVariable:
         offsets = self.offsets
         grid = self.grid if grid is None else grid
         domain_factor = 2 * torch.pi / self.diam
@@ -243,9 +244,9 @@ class SimpleSolenoidalForcing(ForcingFn):
 
     def __init__(
         self,
-        scale=1,
+        scale=1.0,
         diam=1.0,
-        k=1.0,
+        wave_number=1,
         offsets=((0, 0), (0, 0)),
         vorticity=True,
         *args,
@@ -255,7 +256,7 @@ def __init__(
             *args,
             scale=scale,
             diam=diam,
-            wave_number=k,
+            wave_number=wave_number,
             offsets=offsets,
             vorticity=vorticity,
             **kwargs,
@@ -273,7 +274,7 @@ def velocity_eval(
         self,
         grid: Optional[Grid],
         velocity: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
+    ) -> GridVariableVector:
         offsets = self.offsets
         grid = self.grid if grid is None else grid
         domain_factor = 2 * torch.pi / self.diam
@@ -292,7 +293,7 @@ def velocity_eval(
             rot = self.potential(x, y, scale, k)
             u = GridVariable(rot, offsets[0], grid)
             v = GridVariable(-rot, (1 / 2, 1), grid)
-        return tuple((u, v))
+        return GridVariableVector(tuple((u, v)))
 
     def vorticity_eval(
         self,
@@ -339,7 +340,7 @@ def __init__(
         self,
         scale=0.1,
         diam=1.0,
-        k=1.0,
+        wave_number=1,
         offsets=((0, 0), (0, 0)),
         *args,
         **kwargs,
@@ -348,7 +349,7 @@ def __init__(
             *args,
             scale=scale,
             diam=diam,
-            k=k,
+            wave_number=wave_number,
             offsets=offsets,
             **kwargs,
         )