Adaptive Sampling for Hydrodynamic Stability

TL;DR

This paper introduces an adaptive sampling method combining deep generative models and classification to efficiently identify bifurcation boundaries in fluid flow problems, reducing computational costs significantly.

Contribution

It develops a flow-based deep generative model that adaptively refines parameter sampling for bifurcation detection, improving efficiency over previous static sampling methods.

Findings

Achieves accurate bifurcation boundary detection with fewer simulations.

Employs entropy-based uncertainty to guide adaptive sampling.

Scalable to high-dimensional stability analysis.

Abstract

An adaptive sampling approach for efficient detection of bifurcation boundaries in parametrized fluid flow problems is presented herein. The study extends the machine-learning approach of Silvester~(J. Comput. Phys., 553 (2026), 114743), where a classifier network was trained on preselected simulation data to identify bifurcated and nonbifurcated flow regimes. In contrast, the proposed methodology introduces adaptivity through a flow-based deep generative model that automatically refines the sampling of the parameter space. The strategy has two components: a classifier network maps the flow parameters to a bifurcation probability, and a probability density estimation technique (KRnet) for the generation of new samples at each adaptive step. The classifier output provides a probabilistic measure of flow stability, and the Shannon entropy of these predictions is employed as an uncertainty indicator. KRnet is trained to approximate a probability density function that concentrates sampling in regions of high entropy, thereby directing computational effort towards the evolving bifurcation boundary. This coupling between classification and generative modeling establishes a feedback-driven adaptive learning process analogous to error-indicator based refinement in contemporary partial differential equation solution strategies. Starting from a uniform parameter distribution, the new approach achieves accurate bifurcation boundary identification with significantly fewer Navier--Stokes simulations, providing a scalable foundation for high-dimensional stability analysis.