Sensitivity analysis on chaotic dynamical system by Non-Intrusive Least Square Shadowing (NILSS)
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Abstract (arXiv)
This paper develops the non-intrusive formulation of the Least-squares shadowing (LSS) method, for computing the sensitivity of long-time averaged objectives in chaotic dynamical systems. This non-intrusive formulation constrains the computation to only the unstable subspace, greatly reducing the cost of LSS for many problems; moreover, it reparametrizes the LSS problem, requiring only minor modifications to existing tangent solvers. NILSS is demonstrated on a chaotic flow over a backward-facing step simulated with a mesh of 12e3 cells.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 3 Nov 2016.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 1; 2024: 0; 2025: 2.
6 papers.
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- 2021Gradient-free optimization of chaotic acoustics with reservoir computingcited by 4
- 2025OGF: An Online Gradient Flow Method for Optimizing the Statistical Steady-State Time Averages of Unsteady Turbulent Flowscited by 0
- 2025Divergence-kernel method for linear responses of densities and generative modelscited by 0
- 2025Divergence-kernel method for linear responses of densities and generative models4 Sep 2025
- 2025OGF: An Online Gradient Flow Method for Optimizing the Statistical Steady-State Time Averages of Unsteady Turbulent Flows7 Jul 2025
- 2023Low-Variance Gradient Estimation in Unrolled Computation Graphs with ES-Single21 Apr 2023
- 2022Differentiable Programming for Earth System Modeling29 Aug 2022
- 2021Gradients are Not All You Need10 Nov 2021
- 2021Gradient-free optimization of chaotic acoustics with reservoir computing17 Jun 2021