  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.
