Interpretable Multi-Source Data Fusion Through Latent Variable Gaussian Process

TL;DR

This paper introduces a novel multi-source data fusion framework using Latent Variable Gaussian Processes that effectively combines diverse data sources into a single, interpretable predictive model, improving accuracy in sparse-data scenarios.

Contribution

The paper proposes a source-aware data fusion method based on LVGP that accounts for differences in data quality and interpretability, addressing limitations of existing fusion techniques.

Findings

Enhanced prediction accuracy over single-source models

Effective handling of sparse data problems

Ability to interpret source differences via latent variables

Abstract

With the advent of artificial intelligence and machine learning, various domains of science and engineering communities have leveraged data-driven surrogates to model complex systems through fusing numerous sources of information (data) from published papers, patents, open repositories, or other resources. However, not much attention has been paid to the differences in quality and comprehensiveness of the known and unknown underlying physical parameters of the information sources, which could have downstream implications during system optimization. Additionally, existing methods cannot fuse multi-source data into a single predictive model. Towards resolving this issue, a multi-source data fusion framework based on Latent Variable Gaussian Process (LVGP) is proposed. The individual data sources are tagged as a characteristic categorical variable that are mapped into a physically interpretable latent space, allowing the development of source-aware data fusion modeling. Additionally, a dissimilarity metric based on the latent variables of LVGP is introduced to study and understand the differences in the sources of data. The proposed approach is demonstrated on and analyzed through two mathematical and two materials science case studies. From the case studies, it is observed that compared to using single-source and source unaware machine learning models, the proposed multi-source data fusion framework can provide better predictions for sparse-data problems.