Physics-informed machine learning with smoothed particle hydrodynamics: Hierarchy of reduced Lagrangian models of turbulence

Abstract

Construction of efficient and generalizable reduced order models of developed turbulence is a formidable challenge. Bridging the gap between physics-based models and data-driven methods, we introduce a novel approach that involves constructing a hierarchy of learnable and parameterized reduced Lagrangian models based on Smoothed Particle Hydrodynamics (SPH) and machine learning. By training these models on high fidelity Direct Numerical Simulation data sets and comparing their generalizability, we demonstrate the effectiveness of SPH-informed models at predicting statistical and field-based quantities of turbulent flows across a range of resolutions, time scales, and turbulent Mach numbers.