Untrained and Unmatched: Fast and Accurate Zero-Training Classification for Tabular Engineering Data
Name
md_146_9_091705.pdf
Description
Published version
Size
1.12 MB
Format
Adobe PDF
Checksum (MD5)
ac0b8cf6210970cb44bb482300bc31b1
Author(s)
Picard, Cyril
Ahmed, Faez
Date Issued
March 5, 2024
Journal
Journal of Mechanical Design
Publisher
ASME International
Citation
Picard, C., and Ahmed, F. (March 5, 2024). "Untrained and Unmatched: Fast and Accurate Zero-Training Classification for Tabular Engineering Data." ASME. J. Mech. Des. September 2024; 146(9): 091705.
Version
Final published version
Abstract
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 automated machine learning (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.
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Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1115/1.4064811
