Visualization of Uncertainty for Computationally Intensive Simulations Using High Fidelity Emulators

Abstract

Visualization of high-fidelity scientific simulations with high-dimensional inputs and outputs is an important task. Existing high-dimensional data visualization approaches generally assume a substantial amount of data are available or can be generated as needed. However, many of these simulations can be very computationally intensive, taking minutes or hours to run. Analysis and visualization of such expensive simulations poses a challenge. Statistical emulators are frequently used to approximate simulations for statistical analyses. In this work, we propose a visualization tool for an emulator of the simulator and describe how emulators can be used to create effective visualization systems. We choose Gaussian process emulators for this purpose as they enable fast and accurate prediction with uncertainty information. Using these predictions, we design a system that enables visualization of high-dimensional input and output spaces of complex physics simulations. Users of our system can get a detailed understanding of the uncertainties associated with the emulator predictions in both input and output space for a high-dimensional simulation.