Heterogenous Multi-Source Data Fusion Through Input Mapping and Latent Variable Gaussian Process

TL;DR

This paper introduces a novel framework combining input mapping calibration and latent variable Gaussian processes to effectively fuse heterogeneous multi-source data with different input spaces, improving predictive accuracy in engineering models.

Contribution

It proposes a new two-stage data fusion framework that transforms heterogeneous inputs into a unified space and builds a source-aware surrogate model, addressing limitations of existing methods.

Findings

Improved predictive accuracy over single source models.

Effective transformation of heterogeneous input spaces.

Validated on three engineering case studies.

Abstract

Artificial intelligence and machine learning frameworks have served as computationally efficient mapping between inputs and outputs for engineering problems. These mappings have enabled optimization and analysis routines that have warranted superior designs, ingenious material systems and optimized manufacturing processes. A common occurrence in such modeling endeavors is the existence of multiple source of data, each differentiated by fidelity, operating conditions, experimental conditions, and more. Data fusion frameworks have opened the possibility of combining such differentiated sources into single unified models, enabling improved accuracy and knowledge transfer. However, these frameworks encounter limitations when the different sources are heterogeneous in nature, i.e., not sharing the same input parameter space. These heterogeneous input scenarios can occur when the domains differentiated by complexity, scale, and fidelity require different parametrizations. Towards addressing this void, a heterogeneous multi-source data fusion framework is proposed based on input mapping calibration (IMC) and latent variable Gaussian process (LVGP). In the first stage, the IMC algorithm is utilized to transform the heterogeneous input parameter spaces into a unified reference parameter space. In the second stage, a multi-source data fusion model enabled by LVGP is leveraged to build a single source-aware surrogate model on the transformed reference space. The proposed framework is demonstrated and analyzed on three engineering case studies (design of cantilever beam, design of ellipsoidal void and modeling properties of Ti6Al4V alloy). The results indicate that the proposed framework provides improved predictive accuracy over a single source model and transformed but source unaware model.