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AETHER Opt研究
AETHER Opt

AETHER Opt

Generative Design Optimization Engine

研究RHX.LAB experiment

Find verifiable design candidates from ideas and constraints.

AETHER handles performance goals, weight, and manufacturing constraints in early planning and connects to RHXY Plan as a design exploration layer.

它能实现什么

Goal-led exploration

Turn goals and constraints into a design search space.

Candidate comparison

Compare directions by strength, weight, and manufacturability.

Verification handoff

Package promising candidates for NEXUS and RHXY Sim validation.

工作方式

01

Frame

Structure goals, files, constraints, and evaluation criteria.

02

Explore

Generate parametric or topology candidates and narrow the search.

03

Select

Show trade-offs and validation needs so users can decide.

输出示例

AETHER Opt
Experiment preview
Candidate design set
Strength-weight trade-offs
Manufacturing constraint checks
Validation request package

Connected to RHXY Plan design exploration.

Currently in research, focused on verifiable candidate generation and RHXY Sim handoff.

当前状态

2D topology optimizationComplete
3D SIMP optimizationResearch
Manufacturing constraint integrationResearch

技术细节

Goal definition (NL) → Parametric space generation → Topology optimization (SIMP/Level-set) → Candidate geometry generation → NEXUS solver verification → Pareto front visualization → Final candidate selection Manufacturing constraints (minimum thickness, draft angles, overhang limits) are included in the optimization loop to generate only manufacturable geometries.

核心能力

Topology optimization (SIMP, Level-set)
Multi-objective optimization (strength/weight/cost)
Manufacturing constraints (min thickness, draft)
Automated parametric studies
NEXUS solver-integrated verification
Pareto front visualization
AI-driven design suggestions

路线图

2D topology optimizationComplete
3D SIMP optimizationResearch
Manufacturing constraint integrationResearch
NEXUS solver integrationPlanned
AI design suggestionsPlanned

公开范围

Optimization algorithm direction and benchmark results are shared as research notes. Implementation details and trained models are proprietary.

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