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Differentiable code-first CAD: sketches, constraints, SDF geometry, meshing and
FEM simulation composed into one function JAX can differentiate end to end.
The API is not stable. Expect breaking changes.
, free=True, ...). Then the camera makes one full turn around the bracket.]
*The handle under the pointer is a named Vector2 in scene.py, and the
joint it moves is a smooth union of three extrusions. Dragging it writes the
parameter buffer the shader already reads, so the blend follows at frame
rate; releasing it patches the literal in the source. The source is the
model.*檢視完整 README ↗Differentiable code-first CAD: sketches, constraints, SDF geometry, meshing and FEM simulation composed into one function JAX can differentiate end to end.
[!WARNING] The API is not stable. Expect breaking changes.
Dragging the rib_tip handle updates the shader's parameter buffer each frame and rewrites the Vector2 literal in scene.py on release.
sketch vertices ─► constraints ─► SDF ─► mesh ─► FEM solve ─► objective
└─────────────────────────── ∂J/∂θ ◄───────────────────────────┘
One Python program declares every stage, and JAX differentiates through each:
Vector2 parameters.Optimization are declared in the scene beside the geometry.
Each optimization step is a mesh, a solve and an adjoint, projected back onto
the constraints.The end cap's ThermalStudy is meshed and solved with one click.
The heat sink starts overbuilt; its declared Optimization slims it by descending on the gradient of peak temperature with respect to the sketch.
In Sketch mode, dragging a literal work plane by its gizmo rewrites its SketchPlane(origin=...); a plane taken from a face (SketchPlane.on(body.cap("+"))) is instead an expression over the parent feature's parameters, so it is differentiable.
apply/vjp interface, implemented as a
Tesseract and run
in-process, in a container or remotelypip install cadjoint # pin an exact version: the API is not stable
Everything beyond the geometry core is an extra (pip install "cadjoint[fem]",
or uv sync --extra fem in a checkout, one --extra per name):
| Extra | Pulls in |
|---|---|
cuda | GPU JAX (Linux + NVIDIA only; no macOS wheels, so avoid --all-extras on a Mac) |
fem | jax-fem finite-element stack (basix, meshio, petsc4py, gmsh); compiles PETSc |
tesseract | tesseract-core + tesseract-jax, the plugin runtime |
gmsh | Gmsh in-process for the tet10 mesher (GPL, hence its own extra) |
viewer | the playground server (pydantic), the Jupyter widget (anywidget) and the process monitor (psutil) |
editor | playground lint, completion and signature help (ruff, jedi) |
stepcheck | OCCT validation of STEP exports (dev) |
docs | Quarto API reference (quartodoc) |
Installing from a checkout, the platforms CI verifies, and the toolchain the
fem extra needs are in
Getting started.
uvx --from "cadjoint[viewer,editor]" cadjoint-viewer --open # no checkout
uv run --extra viewer --extra editor cadjoint-viewer --open # from a checkout
The editor holds scene.py and the viewport shows the compiled field. Vertex
handles, gizmos, material swatches, face picks, constraint chips, solver runs
and the properties window all write back into the source.
The properties window edits the arguments of the selected call: here the comb's extrusion depth.
The server listens only on localhost but executes the scene's Python on your machine; only run code you trust. The playground guide covers every window, the scenes directory and browser support.
from cadjoint.construction import PolygonProfile, SketchPlane, extrude
from cadjoint.constraints import DistanceConstraint, satisfy_constraints
from cadjoint.geometry import Scalar, Vector2
from cadjoint.meshing import GridSpec, extract_mesh
a = Vector2([0.0, 0.0], free=True, name="a")
b = Vector2([1.0, 0.0], free=True, name="b")
c = Vector2([1.0, 1.0], free=True, name="c")
d = Vector2([0.0, 1.0], free=True, name="d")
DistanceConstraint(a, b, 1.2)
depth = Scalar(0.5, free=True, name="depth")
block = extrude(PolygonProfile([a, b, c, d], plane=SketchPlane()), depth=depth)
satisfy_constraints(block) # projects a and b onto the constraint, in place
grid = GridSpec.from_bounds((-0.5, -0.5, -0.5), (2.0, 2.0, 1.5), 32)
mesh = extract_mesh(block, grid)
functionalize(block) returns a pure function of the parameter values, so
jax.grad of anything downstream reaches a, b, c, d and depth.
Modelling covers
sketches, features and constraints, and the
guides continue to
meshing, a thermal study and an optimization;
examples/fem_bracket_optimization.py runs the full chain from the command
line, with the adjoint checked against finite differences at every boundary.
uv run pytest tests -q --ignore=tests/fem # fast gate
uv run pytest tests/fem -q # needs the fem extra; CalculiX via CADJOINT_CCX
uv run pre-commit install # ruff on commit
The playground UI is a Solid + TypeScript app in frontend/, built into
cadjoint/viewer/static and committed, so installing cadjoint needs no Node
toolchain; its workflow is in
developing the UI.
The same app also builds as a static public demo with no Python server
(npm run dev:static in frontend/); see
frontend/src/static/README.md.
Docs need Quarto and the docs extra:
uv run quartodoc build, then quarto preview.
benchmarks/jax_compile_profile.py splits a worker request into trace, lower,
XLA compile and cache time per program;
research/performance.md
records what each optimization bought.
Inspired by Fidget and Inigo Quilez's distance functions.