Aether generative design.
Generate the geometry. Validate the physics. One model.
Most “generative design” is a shape generator bolted onto a CAD seat — it proposes geometry a separate solver then has to check, and the two never agree. Aether generates and validates under one model: state the loads, constraints and objectives, and it forward-rolls the physics on every candidate it produces. Topology optimization, generative lattices, material-and-process co-design — physics-true, manufacturing-aware, and honest about what it can't yet do.
- Agents
- 12
- Path
- Requirement → Validated part
- Validation
- Simulation in the loop
- Tiers
- Parametric → Surrogate → Sim → FEM
Generation and validation, under one model.
The incumbent generative stack is a CAD add-on, a topology tool, a lattice plugin and a DfM checker that each speak a different file format. Aether is one model — the load path that places material is the load path that validates it, so there is no draw-then-simulate round trip between you and the answer.
Goal-conditioned generation
State the objective — minimum mass at a stiffness target, maximum heat transfer in a volume — and Aether generates geometry to meet it, rather than morphing a shape you already drew.
Physics-driven topology optimization
Material is placed where the simulated load path needs it and removed where it doesn't — SIMP / level-set optimization driven by the same FEA the result is validated against, not a decoupled approximation.
Multi-objective Pareto search
Mass, stiffness, stress, thermal, cost and manufacturability traded off at once. You get a Pareto front of real options with an honest yield funnel, not a single black-box answer.
Constraint handling
Loads, boundary conditions, packaging envelopes, keep-out zones, symmetry and bolt patterns are hard constraints on the search — a candidate that violates one is never proposed.
Simulation-in-the-loop validation
Every candidate is forward-rolled through FEA / CFD / thermal as it is generated. Generative plausibility never overrides a failed physics check — the defining difference from a CAD-bolted generator.
Generative lattices & metamaterials
Graded lattices, TPMS and architected metamaterials generated for stiffness-to-weight, energy absorption or tuned thermal and acoustic response — with their effective properties computed, not assumed.
Material & process co-design
Geometry and material chosen together — couples to the materials discipline so the structure and the alloy or polymer it is printed from are optimized as one problem.
Manufacturing-aware (DfM)
Additive overhang and support, CNC tool access, casting draft and minimum wall enforced during generation — the result is producible by the route you name, not a shape that can't be built.
Inverse design
Target a property or a field — a deflection, a flow profile, a frequency response — and the model searches structures that produce it. The engineering analog of inverse material and molecule design.
Surrogate-accelerated search
A distilled surrogate explores the design space at interactive speed; high-fidelity simulation confirms the finalists. Thousands of candidates evaluated without thousands of full solves.
Parametric & field exploration
Sweep parameters, run Latin-hypercube DOE and sensitivity analysis, and explore field-driven variations — with provenance on every branch of the study.
Honest feasibility
Each result ships validated against the physics, with its margins, the loads it survives, and the manufacturing route it assumes — and an explicit “not feasible yet” when the objective can't be met.
Every part knows that it works.
The governing principle: a generated shape is never presented as a validated part, and a surrogate-ranked candidate is never presented as simulation-confirmed. The four-tier ladder makes that structural — generative plausibility never overrides a failed check.
Parametric & reduced-order
Analytic sizing and parametric sweeps. Milliseconds. For early space exploration and screening — always labelled as such, never a final geometry.
Surrogate-accelerated generation
A distilled surrogate drives the generative search at interactive speed. The tier most exploration lives in — thousands of candidates, ranked.
Simulation-validated
Finalists confirmed by high-fidelity FEA / CFD / thermal against the real load cases. The tier behind a geometry you commit to manufacturing.
First-principles (escalation)
Detailed nonlinear, multiphysics or fatigue analysis. A regime the model cannot yet resolve returns the engine it requires — never a generated part dressed up as validated.
Six design domains, one generator.
Not a plugin per domain — one model whose physics and manufacturing rules change per problem, but whose contract does not.
Brackets, frames, housings and load-bearing parts optimized for mass, stiffness and fatigue under real load cases — validated by the same FEA that drove the search.
Heat sinks, manifolds, ducts and flow paths generated for pressure drop, heat transfer and uniformity, with CFD in the loop on every candidate.
Geometry generated for the printer it ships to — overhang, support, residual stress and minimum feature enforced during generation, not patched after.
Graded and architected structures for stiffness-to-weight, energy absorption, and tuned acoustic or thermal response, with effective properties computed.
The alloy, polymer or composite and the geometry optimized as one problem — coupled to the materials discipline and its property models.
Mass-critical parts where every gram is qualified — generated, simulation-validated and manufacturing-ready, with the margins an engineer can sign off.
Validated, not just generated.
The difference between generative design that ships and generative design that demos is whether the physics ran on every candidate. We report what actually got checked.
Every candidate forward-rolled through the physics
Topology driven by the same solver it's checked against
Mass · stiffness · stress · thermal · cost · DfM
Producible by the route you name
Parametric → surrogate → simulation → first-principles
Margins, load cases and assumptions on every result
One model for the whole loop.
A CAD add-on, a topology tool, a lattice plugin and a DfM checker collapse into one model, one contract, one provenance trail.
Generate and validate, one model.
The incumbents generate geometry a separate solver then has to check. Aether generates and validates under one model, with the physics in the loop on every candidate.
Design cycles, compressed.
What compounds is validated candidates per week, not any single solve being faster.
From goal-conditioned generation to honest feasibility — each emitting results under one integrity contract, with the model version on every candidate.
Every generated candidate is forward-rolled through the real physics — the difference between a producible part and a pretty render.
Manufacturing constraints shape the search, so the optimized geometry is buildable by your route on the first pass.
A design engineer, not a shape generator.
It reads the requirement, generates against the objective, validates in simulation, trades off honestly, and writes the qualification — for an expert audience.
- 01
Reads the requirement
Ingests the loads, the constraints, the envelope, the material and the manufacturing route, and decomposes the brief into a typed optimization problem before any geometry is generated.
- 02
Generates against the objective
Produces geometry to meet the target — minimum mass at a stiffness goal, maximum heat transfer in a volume — rather than morphing an existing shape.
- 03
Validates in simulation
Forward-rolls each candidate through FEA / CFD / thermal as it is generated; a candidate that fails a physics or manufacturing check is discarded, not proposed.
- 04
Trades off honestly
Returns a Pareto front across mass, stress, thermal, cost and manufacturability with calibrated margins — not a single answer that hides the compromises.
- 05
Writes the qualification
Each result ships with the load cases it survives, its margins, the manufacturing route it assumes and the model-version hash — the evidence travels with the part.
- 06
Closes the loop
Test and field results return into the model, so the next generation is optimized against measured behaviour, not just the original assumptions.
Generative design that gets better with every test.
Each test and field result returns into the model, so the geometry it generates next is optimized against measured behaviour — and the gap between the simulated part and the real one closes instead of widening.
“The old tool drew a shape and hoped the solver agreed. This one generates and validates in the same breath — it printed right the first time.”
Generate a part against your requirement.
State the loads, the constraints and the manufacturing route, and Aether generates the geometry and validates it by simulation in the loop — producible the first time. Request access to deploy it behind your firewall, or see how it couples to the rest of the platform.
Generative design is inverse design on geometry — the same model that twins the result.