Purdue University Graduate School
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Multi-fidelity Model with Dimension Reduction

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posted on 2020-12-12, 01:25 authored by Bangde LiuBangde Liu
In scientific and engineering applications, often sufficient low-cost low-fidelity data is available while only a small fractional of high-fidelity data is accessible. The multi-fidelity model integrates a large set of low-cost but biased low-fidelity datasets with a small set of precise but high-cost high-fidelity data to make an accurate inference of quantities of interest. Under many circumstances, the number of model input dimensions is often high in real applications. To simplify the model, dimension reduction is often used. The gradient-free active subspace is employed in this research for dimension reduction. In this work, we build a predictive model for high-dimensional nonlinear problems by integrating the nonlinear multi-fidelity Gaussian progress regression and the gradient-free active subspace method. Numerical results demonstrated that the proposed approach can not only perform effective dimension reduction on the original data but also obtain accurate prediction results thanks to the effective dimension reduction procedure.


Degree Type

  • Master of Science in Mechanical Engineering


  • Mechanical Engineering

Campus location

  • West Lafayette

Advisor/Supervisor/Committee Chair

Guang Lin

Additional Committee Member 2

Ilias Bilionis

Additional Committee Member 3

Baijian Yang