Multi-task Modeling for Engineering Applications with Sparse Data

2026-01-09

AI Summary

This study addresses the challenge of scarce high-fidelity data amid abundant low-fidelity observations in engineering systems by proposing a multitask Gaussian process (MTGP) framework that explicitly models cross-task correlations among output variables and multiple fidelity levels. The approach innovatively integrates multi-source, multi-fidelity information with a structured dependency among multiple outputs, enabling efficient and robust collaborative prediction even under extreme data sparsity. Experimental validation on three representative engineering scenarios—the Forrester function, three-dimensional ellipsoidal cavity modeling, and friction stir welding—demonstrates that the proposed method significantly enhances predictive accuracy, thereby offering effective support for intelligent decision-making in high-cost engineering environments.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Modern engineering and scientific workflows often require simultaneous predictions across related tasks and fidelity levels, where high-fidelity data is scarce and expensive, while low-fidelity data is more abundant. This paper introduces an Multi-Task Gaussian Processes (MTGP) framework tailored for engineering systems characterized by multi-source, multi-fidelity data, addressing challenges of data sparsity and varying task correlations. The proposed framework leverages inter-task relationships across outputs and fidelity levels to improve predictive performance and reduce computational costs. The framework is validated across three representative scenarios: Forrester function benchmark, 3D ellipsoidal void modeling, and friction-stir welding. By quantifying and leveraging inter-task relationships, the proposed MTGP framework offers a robust and scalable solution for predictive modeling in domains with significant computational and experimental costs, supporting informed decision-making and efficient resource utilization.

Problem

• multi-task learning
• multi-fidelity data
• data sparsity
• engineering applications
• predictive modeling

Innovation

• Multi-Task Gaussian Processes
• Multi-fidelity modeling
• Sparse data
• Inter-task correlation
• Engineering applications