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Found 5 papers, 1 papers with code
Heterogenous Multi-Source Data Fusion Through Input Mapping and Latent Variable Gaussian Process
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.
Interpretable Multi-Source Data Fusion Through Latent Variable Gaussian Process
Additionally, a dissimilarity metric based on the latent variables of LVGP is introduced to study and understand the differences in the sources of data.
Mixed-Variable Global Sensitivity Analysis For Knowledge Discovery And Efficient Combinatorial Materials Design
So far, global sensitivity studies have often been limited to design spaces with only quantitative (numerical) design variables.
A Latent Variable Approach for Non-Hierarchical Multi-Fidelity Adaptive Sampling
However, most existing MF methods rely on the hierarchical assumption of fidelity levels or fail to capture the intercorrelation between multiple fidelity levels and utilize it to quantify the value of the future samples and navigate the adaptive sampling.
Rapid Design of Top-Performing Metal-Organic Frameworks with Qualitative Representations of Building Blocks
Our approach provides three main advantages: (i) no specific physical descriptors are required and only building blocks that construct the MOFs are used in global optimization through qualitative representations, (ii) the method is application and property independent, and (iii) the latent variable approach provides an interpretable model of qualitative building blocks with physical justification.
