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.

翻译：随着人工智能和机器学习的发展，科学与工程领域的各个学科已通过融合来自已发表论文、专利、开放存储库或其他资源的多种信息（数据）源，利用数据驱动的代理模型来建模复杂系统。然而，现有研究对信息源中已知与未知基础物理参数在质量和完整性方面的差异关注不足，这些差异可能在系统优化过程中产生下游影响。此外，现有方法无法将多源数据融合到单一的预测模型中。为解决此问题，本文提出了一种基于潜在变量高斯过程的多源数据融合框架。各数据源被标记为特征类别变量，并映射到一个物理可解释的潜在空间，从而能够开发源感知的数据融合建模方法。此外，本文引入了一种基于LVGP潜在变量的相异性度量，用以研究和理解数据源之间的差异。通过两个数学案例和两个材料科学案例研究对所提方法进行了验证与分析。案例研究表明，与使用单源数据及无源感知的机器学习模型相比，所提出的多源数据融合框架能够为稀疏数据问题提供更好的预测性能。