Different goals in multiscale simulations and how to reach them

TL;DR

This paper reviews multiscale simulation techniques, emphasizing their role in data perception and processing, and discusses methods for effectively integrating multiple scales in simulations.

Contribution

It summarizes previous work on multiscale simulations, highlighting different approaches to handle multiple scales simultaneously for improved data analysis.

Findings

Multiscale techniques help distinguish signal from noise.

Using environmental markers introduces historical context into data.

Knowledge-based multiscale handling improves simulation accuracy.

Abstract

In this paper we sum up our works on multiscale programs, mainly simulations. We first start with describing what multiscaling is about, how it helps perceiving signal from a background noise in a ?ow of data for example, for a direct perception by a user or for a further use by another program. We then give three examples of multiscale techniques we used in the past, maintaining a summary, using an environmental marker introducing an history in the data and finally using a knowledge on the behavior of the different scales to really handle them at the same time.