Full Domain Analysis in Fluid Dynamics
This work addresses the problem of understanding complex systems in computational physics and beyond, though it appears incremental as it builds on existing techniques without claiming major breakthroughs.
The paper tackles the challenge of efficiently exploring and analyzing the full solution space in complex, computationally expensive domains like fluid dynamics, by proposing a formal model for full domain analysis that integrates evolutionary optimization, simulation, and machine learning to generate and study diverse flow examples.
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.