KH-PINN: Physics-informed neural networks for Kelvin-Helmholtz instability with spatiotemporal and magnitude multiscale

TL;DR

This paper introduces KH-PINN, a physics-informed neural network framework that effectively reconstructs flow fields and infers parameters for Kelvin-Helmholtz instability, even with sparse, noisy data, by addressing multiscale challenges.

Contribution

The work presents the first application of PINNs to unsteady incompressible flows with variable densities and introduces multiscale embedding and small-velocity amplification strategies.

Findings

KH-PINN accurately reconstructs complex vortex flows.

It infers unknown parameters across various Reynolds numbers.

It demonstrates robustness to noise and few-shot learning scenarios.

Abstract

Prediction of Kelvin-Helmholtz instability (KHI) is crucial across various fields, requiring extensive high-fidelity data. However, experimental data are often sparse and noisy, while simulated data may lack credibility due to discrepancies with real-world configurations and parameters. This underscores the need for field reconstruction and parameter inference from sparse, noisy data, which constitutes inverse problems. Based on the physics-informed neural networks (PINNs), the KH-PINN framework is established in this work to solve the inverse problems of KHI flows. By incorporating the governing physical equations, KH-PINN reconstructs continuous flow fields and infer unknown transport parameters from sparse, noisy observed data. The 2D unsteady incompressible flows with both constant and variable densities are studied. To our knowledge, this is the first application of PINNs to unsteady incompressible flows with variable densities. To address the spatiotemporal multiscale issue and enhance the reconstruction accuracy of small-scale structures, the multiscale embedding (ME) strategy is adopted. To address the magnitude multiscale issue and enhance the reconstruction accuracy of small-magnitude velocities, which are critical for KHI problems, the small-velocity amplification (SVA) strategy is proposed. The results demonstrate that KH-PINN can accurately reconstruct the fields with complex, evolving vortices and infer unknown parameters across a broad range of Reynolds numbers. Additionally, the energy-decaying and entropy-increasing curves are accurately obtained. The effectiveness of ME and SVA is validated through comparative studies, and the anti-noise and few-shot learning capabilities of KH-PINN are also validated. The code for this work is available at https://github.com/CAME-THU/KH-PINN.