Back to Search Start Over

Exploring Multi-Fidelity Data in Materials Science: Challenges, Applications, and Optimized Learning Strategies

Authors :
Ziming Wang
Xiaotong Liu
Haotian Chen
Tao Yang
Yurong He
Source :
Applied Sciences, Vol 13, Iss 24, p 13176 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

Machine learning techniques offer tremendous potential for optimizing resource allocation in solving real-world problems. However, the emergence of multi-fidelity data introduces new challenges. This paper offers an overview of the definition, applications, data preprocessing methodologies, and learning approaches associated with multi-fidelity data. To validate the algorithms, we examine three widely-used learning methods relevant to multi-fidelity data through the design of multi-fidelity datasets that encompass various types of noise. As we expected, employing multi-fidelity data learning methods yields better results compared to solely using high-fidelity data learning methods. Additionally, considering the inherent various types of noise within datasets, the comprehensive correction strategy proves to be the most effective. Moreover, multi-fidelity learning methods facilitate effective decision-making processes by enabling the combination of datasets from various sources. They extract knowledge from lower fidelity data, improving model accuracy compared to models solely relying on high-fidelity data.

Details

Language :
English
ISSN :
20763417
Volume :
13
Issue :
24
Database :
Directory of Open Access Journals
Journal :
Applied Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.b090cb9d804ba9a8110e2cc4c0ec4b
Document Type :
article
Full Text :
https://doi.org/10.3390/app132413176