Dimension reduction for efficient surrogate modeling in high-dimensional applications

Summary

This report studies efficient surrogate modeling for aerospace analyses with high-dimensional inputs and spatio-temporal outputs.

Research contribution

The paper compares singular-value decomposition and random-projection approaches for output reduction and considers sensitivity analysis for input reduction.