A Unified Hierarchical Multi-Task Multi-Fidelity Framework for Data-Efficient Surrogate Modeling in Manufacturing

2026-03-10

AI Summary

This study addresses the challenges of high data requirements and ineffective fusion of multi-source, heterogeneous (multi-fidelity) data in agent-based modeling for manufacturing systems. The authors propose a hierarchical multi-task, multi-fidelity Gaussian process framework that decomposes each task’s response into a shared global trend and task-specific local residuals. By jointly modeling inter-task similarities and fidelity-level relationships, the approach enables efficient data fusion and rigorous uncertainty quantification. Notably, this work presents the first unified integration of multi-task learning and multi-fidelity modeling, accommodating an arbitrary number of tasks, design points, and fidelity levels. In both synthetic benchmarks and a real-world engine surface topography prediction case, the method achieves up to 19% and 23% higher prediction accuracy, respectively, compared to state-of-the-art multi-task models and independent stochastic kriging approaches.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Multiagent Learning
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Surrogate modeling is an essential data-driven technique for quantifying relationships between input variables and system responses in manufacturing and engineering systems. Two major challenges limit its effectiveness: (1) large data requirements for learning complex nonlinear relationships, and (2) heterogeneous data collected from sources with varying fidelity levels. Multi-task learning (MTL) addresses the first challenge by enabling information sharing across related processes, while multi-fidelity modeling addresses the second by accounting for fidelity-dependent uncertainty. However, existing approaches typically address these challenges separately, and no unified framework simultaneously leverages inter-task similarity and fidelity-dependent data characteristics. This paper develops a novel hierarchical multi-task multi-fidelity (H-MT-MF) framework for Gaussian process-based surrogate modeling. The proposed framework decomposes each task's response into a task-specific global trend and a residual local variability component that is jointly learned across tasks using a hierarchical Bayesian formulation. The framework accommodates an arbitrary number of tasks, design points, and fidelity levels while providing predictive uncertainty quantification. We demonstrate the effectiveness of the proposed method using a 1D synthetic example and a real-world engine surface shape prediction case study. Compared to (1) a state-of-the-art MTL model that does not account for fidelity information and (2) a stochastic kriging model that learns tasks independently, the proposed approach improves prediction accuracy by up to 19% and 23%, respectively. The H-MT-MF framework provides a general and extensible solution for surrogate modeling in manufacturing systems characterized by heterogeneous data sources.

Problem

• surrogate modeling
• multi-task learning
• multi-fidelity modeling
• data heterogeneity
• data efficiency

Innovation

• hierarchical multi-task learning
• multi-fidelity modeling
• Gaussian process
• surrogate modeling
• Bayesian formulation