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.

翻译：人工智能与机器学习框架已成为工程问题中输入与输出间计算高效的映射工具。此类映射为优化与分析流程提供了支持，从而催生了卓越的设计方案、创新的材料体系以及优化的制造工艺。在此类建模实践中，常存在多源数据并存的现象，各数据源在保真度、运行条件、实验条件等方面存在差异。数据融合框架为整合此类差异化数据源提供了可能，通过构建统一模型实现了精度提升与知识迁移。然而，当不同数据源本质异构（即不共享相同的输入参数空间）时，现有框架面临局限。这种输入异构场景常出现在因复杂度、尺度与保真度差异而需要不同参数化的领域中。为填补这一空白，本文提出一种基于输入映射校准（IMC）与潜变量高斯过程（LVGP）的异构多源数据融合框架。在第一阶段，利用IMC算法将异构输入参数空间转换至统一的参考参数空间；第二阶段，借助LVGP支持的多源数据融合模型，在转换后的参考空间上构建单一源感知代理模型。通过三个工程案例（悬臂梁设计、椭球空腔设计、Ti6Al4V合金性能建模）对所提框架进行了验证与分析。结果表明，相较于单源模型及经转换但无源感知的模型，该框架具有更优的预测精度。