Extending learned closure models to complex fluid physics (compressible shocks, irregular domains, reactive flows, high Reynolds numbers)

Develop closure models based on scientific machine learning that are applicable to complex fluid-physics regimes, specifically for compressible flows with shock waves, flows in irregular geometries, reactive flows, and high-Reynolds-number conditions, thereby extending beyond simplified benchmarks to these sparsely explored but practically important cases.

Background

In the outlook, the authors emphasize that most existing demonstrations of learned closures focus on simplified PDEs or idealized flow settings, and note concerted efforts to build datasets and benchmarks. They state explicitly that moving beyond such testbeds to more complex physics remains unresolved.

They list specific fluid-mechanics regimes—compressible flows with shocks, irregular domains, reactive flows, and high-Reynolds-number flows—as sparsely explored but important targets for closure modeling with scientific machine learning.