  In engineering design, navigating complex decision-making landscapes demands
a thorough exploration of the design, performance, and constraint spaces, often
impeded by resource-intensive simulations. Data-driven methods can mitigate
this challenge by harnessing historical data to delineate feasible domains,
accelerate optimization, or evaluate designs. However, the implementation of
these methods usually demands machine-learning expertise and multiple trials to
choose the right method and hyperparameters. This makes them less accessible
for numerous engineering situations. Additionally, there is an inherent
trade-off between training speed and accuracy, with faster methods sometimes
compromising precision. In our paper, we demonstrate that a recently released
general-purpose transformer-based classification model, TabPFN, is both fast
and accurate. Notably, it requires no dataset-specific training to assess new
tabular data. TabPFN is a Prior-Data Fitted Network, which undergoes a one-time
offline training across a broad spectrum of synthetic datasets and performs
in-context learning. We evaluated TabPFN's efficacy across eight engineering
design classification problems, contrasting it with seven other algorithms,
including a state-of-the-art AutoML method. For these classification
challenges, TabPFN consistently outperforms in speed and accuracy. It is also
the most data-efficient and provides the added advantage of being
differentiable and giving uncertainty estimates. Our findings advocate for the
potential of pre-trained models that learn from synthetic data and require no
domain-specific tuning to make data-driven engineering design accessible to a
broader community and open ways to efficient general-purpose models valid
across applications. Furthermore, we share a benchmark problem set for
evaluating new classification algorithms in engineering design.
