EngiBench: A Framework for Data-Driven Engineering Design Research
This work addresses the problem of time-consuming and domain-specific engineering design optimization for researchers and practitioners by providing a modular framework, though it is incremental as it builds on existing optimization and machine learning techniques.
The authors tackled the challenge of engineering design optimization by introducing EngiBench, an open-source library and datasets that provide a unified API and benchmarks across domains like aeronautics and photonics, enabling reproducible comparisons of algorithms and showing that standard methods struggle with constrained design manifolds.
Engineering design optimization seeks to automatically determine the shapes, topologies, or parameters of components that maximize performance under given conditions. This process often depends on physics-based simulations, which are difficult to install, computationally expensive, and require domain-specific expertise. To mitigate these challenges, we introduce EngiBench, the first open-source library and datasets spanning diverse domains for data-driven engineering design. EngiBench provides a unified API and a curated set of benchmarks -- covering aeronautics, heat conduction, photonics, and more -- that enable fair, reproducible comparisons of optimization and machine learning algorithms, such as generative or surrogate models. We also release EngiOpt, a companion library offering a collection of such algorithms compatible with the EngiBench interface. Both libraries are modular, letting users plug in novel algorithms or problems, automate end-to-end experiment workflows, and leverage built-in utilities for visualization, dataset generation, feasibility checks, and performance analysis. We demonstrate their versatility through experiments comparing state-of-the-art techniques across multiple engineering design problems, an undertaking that was previously prohibitively time-consuming to perform. Finally, we show that these problems pose significant challenges for standard machine learning methods due to highly sensitive and constrained design manifolds.