Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study
Name
jcise-25-1161.pdf
Description
Published version
Size
973.43 KB
Format
Adobe PDF
Checksum (MD5)
77e4646e65441f52a054b19ffa237a0c
Author(s)
Samuel, Kaira M
Ahmed, Faez
Date Issued
August 25, 2025
Journal
Journal of Computing and Information Science in Engineering
Publisher
ASME International
Citation
Samuel, K. M., and Ahmed, F. (August 25, 2025). "Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study." ASME. J. Comput. Inf. Sci. Eng. October 2025; 25(10): 101003.
Version
Final published version
Abstract
Engineering problems that apply machine learning often involve computationally intensive methods but rely on limited datasets. As engineering data evolve with new designs and constraints, models must incorporate new knowledge over time. However, high computational costs make retraining models from scratch infeasible. Continual learning (CL) offers a promising solution by enabling models to learn from sequential data while mitigating catastrophic forgetting, where a model forgets previously learned mappings. This work introduces CL to engineering design by benchmarking several CL methods on representative regression tasks. We apply these strategies to five engineering datasets and construct nine new engineering CL benchmarks to evaluate their ability to address forgetting and improve generalization. Preliminary results show that applying existing CL methods to these tasks improves performance over naive fine-tuning. In particular, the replay strategy achieved performance comparable to retraining in several benchmarks while reducing training time by nearly half, demonstrating its potential for real-world engineering workflows.
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1115/1.4069113
