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Updated: Jan 12, 2026

Surrogate Model Development for Digital Experiments in Welding

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

Meta-Learning-Based Surrogate Models for Efficient Hyperparameter Optimization.

Liping Deng, Maziar Raissi, MingQing Xiao

IEEE Transactions on Pattern Analysis and Machine Intelligence

|November 6, 2025

Summary

This summary is machine-generated.

This study introduces a novel meta-learning surrogate model for hyperparameter optimization. It leverages historical task data to improve performance prediction, outperforming traditional methods.

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Surrogate Model Development for Digital Experiments in Welding

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Published on: December 6, 2024

Area of Science:

Machine Learning
Artificial Intelligence
Data Science

Background:

Sequential Model-Based Optimization (SMBO) is crucial for machine learning hyperparameter tuning.
Traditional surrogate models (e.g., Gaussian processes, random forests) lack the ability to incorporate historical task data, limiting their efficiency.
This limitation hinders hyperparameter optimization in diverse and evolving machine learning applications.

Purpose of the Study:

To propose a novel meta-learning-based surrogate model for efficient and effective hyperparameter optimization.
To address the limitations of existing surrogate models by incorporating meta-knowledge from historical tasks.
To enhance the prediction accuracy of the hyperparameter response surface with fewer trials on new tasks.

Main Methods:

Developed a meta-learning-based surrogate model inspired by convolutional neural processes.
Trained the surrogate model on meta-knowledge derived from a wide array of historical machine learning tasks.
Applied the model to hyperparameter selection for Support Vector Machines (SVM), Residual Neural Networks (ResNet), and Vision Transformers (ViT) across numerous real-world classification datasets.

Main Results:

The proposed meta-learning surrogate model demonstrated superior performance compared to existing surrogate models.
Accurate prediction of the hyperparameter response surface was achieved even with a limited number of trials on new tasks.
Empirical results confirmed the effectiveness of meta-learning in enhancing hyperparameter optimization.

Conclusions:

Meta-learning offers a powerful approach to significantly improve hyperparameter optimization efficiency and effectiveness.
The novel surrogate model provides a more robust and adaptable solution for hyperparameter tuning across various machine learning models and datasets.
This work highlights the potential of leveraging past experiences across tasks to accelerate future machine learning model development.