# The Wasserstein Distances Between Pushed-Forward Measures with Applications to Uncertainty Quantification

## TL;DR

This paper demonstrates that the Wasserstein metric effectively measures the difference between push-forward measures in uncertainty quantification, providing bounds and insights into how approximations of system responses impact the distribution of outputs.

## Contribution

The authors establish bounds on Wasserstein distances between push-forward measures based on function approximations, highlighting the metric's suitability over traditional density-based measures.

## Key findings

- Wasserstein distance bounds for $f ext{-}
ho$ and $g ext{-}
ho$ are derived.
- Lower bounds for $p=1,2$ are provided.
- Applications to numerical methods in uncertainty quantification are discussed.

## Abstract

In the study of dynamical and physical systems, the input parameters are often uncertain or randomly distributed according to a measure $\varrho$. The system's response $f$ pushes forward $\varrho$ to a new measure $f\circ \varrho$ which we would like to study. However, we might not have access to $f$ but only to its approximation $g$. We thus arrive at a fundamental question -- if $f$ and $g$ are close in $L^q$, does $g\circ \varrho$ approximate $f\circ \varrho$ well, and in what sense? Previously, we demonstrated that the answer to this question might be negative in terms of the $L^p$ distance between probability density functions (PDF). Here we show that the Wasserstein metric is the proper framework for this question. For any $p\geq 1$, we bound the Wasserstein distance $W_p (f\circ \varrho , g\circ \varrho) $ from above by $\|f-g\|_{q}$. Furthermore, we provide lower bounds for the cases of $p=1,2$. Finally, we apply our theory to the analysis of common numerical methods in the field of computational uncertainty quantification.