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Multi-task Modeling for Engineering Applications with Sparse Data
Yigitcan Comlek, R. Murali Krishnan, Sandipp Krishnan Ravi +7
Modern engineering and scientific workflows often require simultaneous predictions across related tasks and fidelity levels, where high-fidelity data is scarce and expensive, while…
Heterogenous Multi-Source Data Fusion Through Input Mapping and Latent Variable Gaussian Process
Yigitcan Comlek, Sandipp Krishnan Ravi, Piyush Pandita +3
Artificial intelligence and machine learning frameworks have served as computationally efficient mapping between inputs and outputs for engineering problems. These mappings have en…
Interpretable Multi-Source Data Fusion Through Latent Variable Gaussian Process
Sandipp Krishnan Ravi, Yigitcan Comlek, Arjun Pathak +9
With the advent of artificial intelligence and machine learning, various domains of science and engineering communities have leveraged data-driven surrogates to model complex syste…
Mixed-Variable Global Sensitivity Analysis For Knowledge Discovery And Efficient Combinatorial Materials Design
Yigitcan Comlek, Liwei Wang, Wei Chen
Global Sensitivity Analysis (GSA) is the study of the influence of any given inputs on the outputs of a model. In the context of engineering design, GSA has been widely used to und…
A Latent Variable Approach for Non-Hierarchical Multi-Fidelity Adaptive Sampling
Yi-Ping Chen, Liwei Wang, Yigitcan Comlek +1
Multi-fidelity (MF) methods are gaining popularity for enhancing surrogate modeling and design optimization by incorporating data from various low-fidelity (LF) models. While most…