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BikeBench: A Bicycle Design Benchmark for Generative Models with Objectives and Constraints
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Introduces BikeBench, a constrained multi-objective bicycle design benchmark with new synthetic (1.6M) and human-rated (10,350 ratings) datasets, benchmarking LLMs, tabular generative models, optimization-augmented generative models, and optimizers.
Problem
Evaluating generative models on parametric engineering design under multiple real-world objectives and hard constraints, where models must satisfy physics, human guidelines, and constraints rather than only match data distributions.
Approach
A design benchmark that scores generated bicycle designs on quality (hypervolume over constraint-feasible designs), constraint violation, similarity to data (MMD), and DPP diversity, using surrogate evaluators for aerodynamics, structures, ergonomics, usability, and aesthetics, and compares LLMs, tabular GMs, optimization-augmented GMs and optimizers side-by-side.
Key results
- OA-DDPM-G achieved the best unconditional design quality (0.0195) and similarity (0.037), while GPT-5-high was a clear underperformer with 0.448 constraint violations per design unconditionally and 0.363 similarity.
- Optimization-augmented generative models achieved the best quality, constraint violation, and similarity scores in both unconditional and conditional generation.
- The dataset baseline averages 2.7 constraint violations per design, with 95% of designs failing the planar safety factor constraint.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add optimization-augmented training (performance-aware loss) to DDPM | DDPM generative model · Unconditional and conditional generation | Design quality and constraint violation | Helped |
| Use LLM (GPT-5-high) for design generation with in-context examples | LLM design generation · Unconditional and conditional generation | Design quality, constraint violation, similarity | Hurt |
| Train unconditional tabular GANs (CTGAN/TVAE) with rejection sampling | CTGAN and TVAE · Unconditional and conditional generation | Design quality vs dataset subsampling | Hurt |
| Use CTGAN as tabular generator rather than other models | CTGAN · Unconditional generation | Diversity (DPP, lower better) | Helped |
| Use classic optimization (gradient descent, NSGA-II) instead of generative models | Optimization baselines · Unconditional generation | Design quality | Hurt |
Surprising findings
- Classic optimization (gradient descent, NSGA-II) was relatively unimpressive, with generative models outperforming it even when amortizing budget over only 100 designs.
- CTGAN, a performance-agnostic tabular generator, achieved the highest diversity score, while sampling only valid dataset designs was a strong baseline.
Limitations
- Surrogate models may lack robustness outside their training data support.
- Design representation covers only conventional diamond-frame bikes, excluding recumbents and suspension.
- Closed-form geometric checks are not comprehensive and do not guarantee feasibility.
Experimental setup
Datasets: BIKED FRAMED CFD cyclist aerodynamics simulations
Benchmarks: BikeBench
Metrics: design quality (hypervolume) constraint violation similarity to data (MMD) design diversity (DPP)
Baselines: random sampling rejection sampling CTGAN TVAE gradient descent NSGA-II
Builds on: BIKED FRAMED CTGAN PaDGAN NSGA-II
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