Full Domain Analysis in Fluid Dynamics

TL;DR

This paper introduces the concept of full domain analysis in fluid dynamics, leveraging optimization, simulation, and machine learning to explore and understand complex flow behaviors efficiently.

Contribution

It formalizes the concept of full domain analysis, reviews current methods, and demonstrates its potential to enhance understanding of complex fluid systems.

Findings

Formal model for full domain analysis proposed

Current state of the art reviewed

Example shows insights gained from full domain analysis

Abstract

Novel techniques in evolutionary optimization, simulation and machine learning allow for a broad analysis of domains like fluid dynamics, in which computation is expensive and flow behavior is complex. Under the term of full domain analysis we understand the ability to efficiently determine the full space of solutions in a problem domain, and analyze the behavior of those solutions in an accessible and interactive manner. The goal of full domain analysis is to deepen our understanding of domains by generating many examples of flow, their diversification, optimization and analysis. We define a formal model for full domain analysis, its current state of the art, and requirements of subcomponents. Finally, an example is given to show what we can learn by using full domain analysis. Full domain analysis, rooted in optimization and machine learning, can be a helpful tool in understanding complex systems in computational physics and beyond.