Sensitivity Analysis

Numerical Optimization
Sensitivity Analysis


Developing iterative sampling methods that employ sensitivity analysis and parametric surrogate models to determine the most important parameters for a given problem and use this information to reduce the numerical optimization search space.

Sensitivity analysis can be used to reduce the number of parameters required for the numerical optimization. We will examine the performance of different sensitivity analysis methods such as variance-based methods, methods that change one parameter at a time, and active subspace methods. We will also explore multilevel approaches that use low-fidelity models to cheaply predict parameter sensitivities.


Todd Munson

Mathematics and Computer Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Building 240, Argonne, IL 60439