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Data-Driven Estimation of Temporal-Sampling Errors in Unsteady Flows

EasyChair Preprint no. 6891

14 pagesDate: October 19, 2021

Abstract

While computer simulations typically store data at the highest available spatial resolution, it is often infeasible to do so for the temporal dimension. Instead, the common practice is to store data at regular intervals, the frequency of which is strictly limited by the available storage and I/O bandwidth. However, this manner of temporal subsampling can cause significant errors in subsequent analysis steps. More importantly, since the intermediate data is lost, there is no direct way of measuring this error after the fact. One particularly important use case that is affected is the analysis of unsteady flows using pathlines, as it depends on an accurate interpolation across time. Although the potential problem with temporal undersampling is widely acknowledged, there currently does not exist a practical way to estimate the potential impact.

This paper presents a simple-to-implement yet powerful technique to estimate the error in pathlines due to temporal subsampling. Given an unsteady flow, we compute pathlines at the given temporal resolution as well as subsamples thereof. We then compute the error induced due to various levels of subsampling and use it to estimate the error between the given resolution and the unknown ground truth. Using two turbulent flows, we demonstrate that our approach, for the first time, provides an accurate, a posteriori error estimate for pathline computations. This estimate will enable scientists to better understand the uncertainties involved in pathline-based analysis techniques and can lead to new uncertainty visualization approaches using the predicted errors.

Keyphrases: Sampling errors, temporal resolution, Uncertainty Visualization, unsteady flow

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
@Booklet{EasyChair:6891,
  author = {Harsh Bhatia and Steve N Petruzza and Rushil Anirudh and Attila G Gyulassy and Robert M Kirby and Valerio Pascucci and Peer-Timo Bremer},
  title = {Data-Driven Estimation of Temporal-Sampling Errors in Unsteady Flows},
  howpublished = {EasyChair Preprint no. 6891},

  year = {EasyChair, 2021}}
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