Interval Reduced Order Surrogate Modelling Framework for Uncertainty Quantification
Published in AIAA SciTech 2024 , 2024
This paper introduces a non-intrusive framework for epistemic surrogate modeling, leveraging interval proper orthogonal decomposition (interval POD) and interval polynomial chaos expansion (interval PCE) to handle interval observations, addressing a major limitation in existing frameworks. By integrating POD for interval data with PCE for interval observations, the framework allows for the consideration of non-scalar data, such as intervals, providing a more comprehensive approach to physical system modeling that captures additional information.
Download here