Skip to content

features.md

Latest commit

 

History

History
43 lines (36 loc) · 2.89 KB

features.md

File metadata and controls

43 lines (36 loc) · 2.89 KB
Raw

Supported Features

The Julia package lives in src/julia/SSP and the Python package in src/python/ssp_topopt. The table below tracks what each one currently implements; please keep it up to date when adding or removing functionality.

Feature Julia (SSP) Python (ssp_topopt)
Conic ("hat") filter ✅ conic_filter ✅ conic_filter
Filter radius from an eroded threshold point ❌ ✅ get_conic_radius_from_eta_e
Plain tanh projection ❌ (internal only) ✅ tanh_projection
First-order subpixel smoothing (SSP1), linear interpolation ✅ ssp1_linear ✅ ssp1_bilinear
First-order subpixel smoothing (SSP1), cubic interpolation ✅ ssp1 ❌
Second-order subpixel smoothing (SSP2), differentiable through topology changes ✅ ssp2 ✅ ssp2
Finite and infinite projection strength (0 ≤ β ≤ ∞) ✅ ✅
Dilation/erosion of the projected contour ✅ dilation_distance argument ❌
Minimum-lengthscale constraints for solid and void ✅ constraint_solid, constraint_void ❌
Lengthscale constraints compatible with any SSP order ✅ (constraints act on rho_filtered/rho_projected) ❌
Reverse-mode automatic differentiation ✅ hand-written adjoints, exposed to Zygote.jl and friends through a ChainRulesCore.jl extension ✅ through JAX (grad, jit, vmap)
Dimensionality N-dimensional code paths (only 2D is currently tested) 2D only
Periodic filter axes ❌ ✅ periodic_axes argument of conic_filter
Low-level init/solve!/adjoint_solve! API with reduced allocations ✅ ❌
Explicit control over padding/boundary conditions, kernels, interpolation, and projection target points ✅ (low-level API) ❌

Known limitations

  • The subpixel fill factor is the analytic expression for a circular smoothing kernel, so both implementations assume an isotropic grid (dx == dy). Julia asserts that all grid steps are equal; Python takes a single scalar resolution in the projection routines (conic_filter does accept an anisotropic resolution).
  • Only 2D usage is covered by the tests and examples in this repository, even though the Julia routines are written generically over the number of dimensions.
  • The high-level Julia conic_filter always pads by replicating the boundary values. Other padding styles (FillPadding, Inner) are only reachable through the low-level API.

Not yet supported

Contributions welcome — these are known gaps rather than fundamental limitations:

  • Python: cubic-interpolation SSP1, dilation/erosion, and minimum-lengthscale constraints.
  • Python: a low-level API with reusable workspaces.
  • Julia: get_conic_radius_from_eta_e-style helpers and periodic filter axes.
  • Both: validated 3D usage and anisotropic grid spacings in the projection.