用优化与机器学习实现流体动力学的全域分析，高效探索复杂流动解空间。
Full Domain Analysis in Fluid Dynamics
- 结合进化优化与机器学习，系统化探索流体问题的全部可能解
- 实现解空间的高效生成与交互式行为分析，提升对复杂流动的理解
- 适合计算物理、工程仿真等领域研究者用于深度洞察复杂系统
进化优化、模拟与机器学习的新技术使得对流体动力学等计算成本高、流动行为复杂的领域进行广泛分析成为可能。所谓全域分析，是指高效确定问题域中所有解的空间，并以可访问、可交互的方式分析这些解的行为。其目标是通过生成大量流动实例，实现解的多样化、优化与分析，从而深化对领域的理解。本文定义了全域分析的形式化模型、当前技术水平及子组件需求，并通过一个实例展示其应用价值。全域分析根植于优化与机器学习，可作为理解计算物理及其他复杂系统的重要工具。
原文摘要 · Abstract (English)
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.
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