no code implementations • 6 Feb 2024 • Sandipp Krishnan Ravi, Yigitcan Comlek, Wei Chen, Arjun Pathak, Vipul Gupta, Rajnikant Umretiya, Andrew Hoffman, Ghanshyam Pilania, Piyush Pandita, Sayan Ghosh, Nathaniel Mckeever, Liping Wang
Towards resolving this issue, a multi-source data fusion framework based on Latent Variable Gaussian Process (LVGP) is proposed.
no code implementations • 23 Oct 2023 • Yigitcan Comlek, LiWei Wang, Wei Chen
So far, global sensitivity studies have often been limited to design spaces with only quantitative (numerical) design variables.
no code implementations • 5 Oct 2023 • Yi-Ping Chen, LiWei Wang, Yigitcan Comlek, Wei Chen
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.
1 code implementation • 17 Feb 2023 • Yigitcan Comlek, Thang Duc Pham, Randall Snurr, Wei Chen
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.