Data reconstruction for complex flows using AI: Recent progress, obstacles, and perspectives

- In recent years the fluid mechanics community has been intensely focused on pur-suing solutions to its long-standing open problems by exploiting the new machine learning (ML) approaches. The exchange between ML and fluid mechanics is bringing important paybacks in both directions. The first is benefiting from new physics-inspired ML methods and a scientific playground to perform quantitative benchmarks, whilst the latter has been open to a large set of new tools inherently well suited to deal with big data, flexible in scope, and capable of revealing unknown correlations. A special case is the problem of modeling missing information of partially observable systems. The aim of this paper is to review some of the ML algorithms that are playing an important role in the current developments in this field, to uncover potential avenues, and to discuss the open challenges for applications to fluid mechanics.

Automated summary

In recent years, the fluid mechanics community has been actively exploring new machine learning approaches to solve long-standing problems. The exchange between ML and fluid mechanics has led to significant advancements in both fields. ML benefits from physics-inspired methods and a scientific environment for quantitative testing, while fluid mechanics benefits from tools that can handle big data, have flexible scope, and reveal unknown correlations. This paper reviews ML algorithms that are important in the current developments in fluid mechanics and discusses the open challenges for their application.