Beyond the Feature Tree

For 35 years, CAD has been built on the same foundation: boundary representation (B-rep). You sketch, you extrude, you fillet — and the software stores surfaces, edges, and vertices. This paradigm is mature, powerful, and deeply embedded in industrial workflows. It is also reaching its limits.

NeuroCAD is built on a different mathematical substrate: signed distance fields (SDF). Instead of tracking where the boundary is, NeuroCAD tracks how far every point in space is from the boundary. This is not a rendering trick or a post-processing step — it is the native geometric truth of the entire platform.

The consequences are far-reaching. Booleans that never fail. Lattices that compose seamlessly with solid geometry. Gradients that flow through the entire design graph. Every design state reproducible from its graph representation, deterministically, on any machine, at any time.

This article surveys the NeuroCAD platform — not as a feature list, but as a coherent engineering system.

The Mathematical Foundation: Signed Distance Fields

A signed distance field is a scalar function defined over three-dimensional space. For every point (x, y, z), it returns a single number:

Negative inside the solid
Positive outside the solid
Zero on the surface

Think of a weather map for geometry. Where B-rep stores surface patches and their connectivity, SDF stores a single mathematical function. This means:

Booleans are always robust. Union, intersection, and difference are simple min/max operations on the field. There are no edges to split, no faces to retriangulate, no topology surgery to fail.

Lattices are native. A gyroid or diamond lattice is just another SDF. Compose it with a solid body via a smooth blend operation, and the result is a single coherent field — no stitching, no watertightness checks.

Gradients exist everywhere. Because the SDF is a continuous function, its gradient is defined at every point. This enables differentiable CAD: optimization algorithms that can see how changing any parameter affects the final geometry.

Four Graphs, One Truth

NeuroCAD’s architecture is organized around four interconnected graphs, each responsible for a distinct concern:

The SDF Graph — Geometric Truth

Every primitive, every Boolean, every lattice, every blend is a node in a directed acyclic graph whose terminal output is a signed distance field. There is no separate “render mesh” or “analysis mesh” — the SDF is the single source of geometric truth.

The Distributed Constraint Graph — Parametric Intelligence

When you sketch a profile and constrain it — parallel, tangent, coincident, dimension — those constraints form a graph that the solver evaluates incrementally. Change one dimension and only the downstream dependencies recompute. The solver is deterministic: the same constraint graph always produces the same geometry, on any machine, at any time.

The E-Graph — Automatic Optimization

E-graphs (equality saturation) are a technique from program optimization applied to geometry. Given a complex SDF tree with redundant or suboptimal operations, the e-graph discovers equivalent but simpler representations — smaller memory footprint, faster evaluation, same geometry. This runs transparently; the designer never sees it, but benefits from it on every interaction.

The Commit DAG — Deterministic Replay and Collaboration

Every design state is a node in a content-addressed commit DAG. Branches, merges, and history are first-class citizens of the geometry kernel — not bolted-on file versioning. This enables deterministic replay (reconstruct any past state exactly), typed three-way merge for collaboration, and audit trails for regulated industries.

Browser-Native: No Install, No Server Round-Trip

NeuroCAD runs entirely in the browser. There is no installer, no OS lock-in, and no cloud dependency for core modeling.

This is possible because:

Rust compiles to WebAssembly. The entire geometry kernel — SDF evaluation, constraint solving, Boolean operations, lattice generation — runs at near-native speed in the browser. No server round-trip. No latency.
WebGPU provides GPU acceleration. Field evaluation, mesh generation, and real-time rendering run on the GPU through the modern WebGPU API — not the aging WebGL. Compute shaders enable parallel field queries and mesh extraction that would saturate a CPU.
Web Workers keep the UI responsive. The kernel runs in a background thread. The interface never freezes, even during heavy Boolean operations or lattice generation on complex parts.

This architecture also has a security implication: your design data never leaves your browser unless you choose to export it. For European engineering firms subject to GDPR, ITAR, or internal data sovereignty policies, this is a decisive advantage over cloud-rendering CAD platforms.

The Feature Landscape

NeuroCAD is shipping in phases, with each phase building on the mathematical foundation of the previous one.

Core Geometry (Phase 1)

The foundation: sketch and constraint authoring with a deterministic solver, SDF primitives and Boolean operations, field evaluation and preview meshing, STL export, and deterministic replay. Everything a designer needs to create, modify, and export solid geometry.

Features and Surfaces (Phase 2)

The traditional CAD toolkit rebuilt on the SDF substrate: extrude, revolve, fillet, chamfer, shell, and pattern operations that preserve parametric history. Surface modeling with loft, sweep, trim, patch, and blend — NURBS and subdivision surfaces with G0/G1/G2 continuity control. Feature-preserving Booleans that maintain design intent through topology changes.

Organic and Optimization (Phase 3)

Where NeuroCAD diverges from traditional CAD: field-native organic modeling with PDE-driven shape evolution (mean curvature flow, reaction-diffusion, Laplace-Beltrami operators). Differentiable CAD with gradient exposure through the entire design graph for optimization. Material fields with continuous grading from one material zone to another. Multi-scale composition — macro structure, micro lattice, and material grading coordinated as a single design.

Production Ready (Phase 4)

Manufacturing governance: process constraints (minimum wall thickness, draft angles, overhang limits) that govern geometry evolution in real time — enforced, not just analyzed. Lattice engineering with graded parametric unit cells (gyroid, diamond, octet). Metrology and inspection with scan-to-CAD reconstruction, tolerance checking, and deviation evaluation. Topology optimization natively on the SDF substrate.

Pure Rust: Memory Safety, Determinism, Zero Runtime Cost

The NeuroCAD kernel is written in pure Rust — a deliberate engineering choice with tangible consequences:

Memory safety without garbage collection. Rust’s ownership system eliminates entire categories of bugs (use-after-free, buffer overflows, data races) at compile time, without the runtime overhead of a garbage collector. For a CAD kernel that must run predictably in a browser, this is essential.

Deterministic execution. Rust’s strict aliasing rules and lack of undefined behavior align with NeuroCAD’s deterministic replay guarantee. The same code produces the same result, on any platform, every time.

WASM as a first-class target. The wasm-pack toolchain compiles Rust directly to WebAssembly. There is no intermediate layer, no emulation, no performance penalty.

Modular architecture. The kernel is organized into 13 crates with clear boundaries: SDF evaluation, constraint solving, e-graph optimization, BVH acceleration, mesh generation, and more. Each crate can be tested, benchmarked, and evolved independently.

Manufacturing Governance: Enforced, Not Analyzed

Traditional DFM (design for manufacturing) works as a post-design check: you model the part, then run an analysis tool, then fix the violations, then re-analyze. This loop is slow and error-prone.

NeuroCAD’s manufacturing governance inverts the workflow. Process constraints — minimum wall thickness for metal AM, draft angles for injection molding, overhang limits for FDM — are declared as rules that actively govern geometry evolution. When you modify a surface, the kernel checks in real time whether the change violates any active constraint. If it does, the violation is surfaced immediately.

This is not a suggestion. This is governance: the system enforces constraints, not just reports them after the fact.

The Integration Surface

NeuroCAD is not an island. The platform provides:

Multi-format export: STL for 3D printing, STEP with semantic overlays for traditional CAD interchange, and field-native formats for downstream simulation.
MOOSE bridge: Direct coupling to the MOOSE finite element framework for authoritative structural, thermal, and multiphysics validation.
AI orchestration boundary: A defined interface where AI-generated geometry proposals enter the system. Proposals are validated against the SDF truth, manufacturing constraints, and design history — never accepted as source of truth.
Scripting and automation: A headless mode with full SDK access for batch processing, design space exploration, and integration into automated workflows.

What This Means for Engineers

NeuroCAD is not “another CAD tool.” It is a different category of engineering platform:

Geometry that cannot break. SDF Booleans do not fail. Lattices do not leak. Topology changes do not require manual healing.
Designs that never diverge. Deterministic replay means every design state is reproducible — across machines, across time, across teams.
Optimization that sees everything. Differentiable CAD exposes gradients through the entire design graph, enabling lawful geometry optimization that traditional CAD cannot approach.
Freedom from installation. Open a browser tab. Start designing. No IT ticket, no license server, no OS dependency.
European data sovereignty. Your design data stays in your browser. No cloud upload, no foreign jurisdiction, no compliance headache.

NeuroCAD is built from mathematics, not legacy. That difference matters.

NeuroCAD: The Next-Generation CAD Platform Built on Mathematics, Not Legacy