An adjoint-based approach for finding invariant solutions of Navier-Stokes equations
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Abstract (arXiv)
We consider the incompressible Navier--Stokes equations with periodic boundary conditions and time-independent forcing. For this type of flow, we derive adjoint equations whose trajectories converge asymptotically to the equilibrium and traveling wave solutions of the Navier--Stokes equations. Using the adjoint equations, arbitrary initial conditions evolve to the vicinity of a (relative) equilibrium at which point a few Newton-type iterations yield the desired (relative) equilibrium solution. We apply this adjoint-based method to a chaotic two-dimensional Kolmogorov flow. A convergence rate of 100% is observed, leading to the discovery of 21 new steady state and traveling wave solutions at Reynolds number Re=40. Some of the new invariant solutions have spatially localized structures that were previously believed to only exist on domains with large aspect ratios. We show that one of the newly found steady state solutions underpins the temporal intermittencies, i.e., high energy dissipation episodes of the flow. More precisely, it is shown that each intermittent episode of a generic turbulent trajectory corresponds to its close passage to this equilibrium solution.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 Aug 2015.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 1; 2024: 0; 2025: 0; 2026: 1.
5 papers.
- 2021Output-weighted and relative entropy loss functions for deep learning precursors of extreme eventscited by 8
- 2022Modelling spatiotemporal turbulent dynamics with the convolutional autoencoder echo state networkcited by 4
- 2021Model-assisted deep learning of rare extreme events from partial observationscited by 3
- 2023Building symmetries into data-driven manifold dynamics models for complex flows: application to two-dimensional Kolmogorov flowcited by 2
- 2026Uncovering Extreme Event Mechanisms for Prediction and Control with Sensitivity-Balanced Projectionscited by 0
- 2026Uncovering Extreme Event Mechanisms for Prediction and Control with Sensitivity-Balanced Projections4 Jun 2026
- 2023Building symmetries into data-driven manifold dynamics models for complex flows: application to two-dimensional Kolmogorov flow15 Dec 2023
- 2022Modelling spatiotemporal turbulent dynamics with the convolutional autoencoder echo state network21 Nov 2022
- 2021Output-weighted and relative entropy loss functions for deep learning precursors of extreme events1 Dec 2021
- 2021Model-assisted deep learning of rare extreme events from partial observations4 Nov 2021