2025/05/01 by Pranshul Thakur, Siva Nadarajah, Thakur, Pranshul +1
Computer Science · Physics and Astronomy · #34A34 #37A99 #37D20 #37D45 #37N30 #46N40 #65P99 #76F20 #Computational Physics and Python Applications #FOS: Mathematics #Numerical Analysis (math.NA) #Optimization and Control (math.OC) #Quantum chaos and dynamical systems
paper · pdf · doi:10.48550/arxiv.2505.00838
openalex publication_date 2025/05/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Adjoint-based sensitivity analysis is of interest in computational science due to its ability to compute sensitivities at a lower cost with respect to several design parameters. However, conventional sensitivity analysis methods fail in the presence of chaotic flows. Popular approaches to chaotic sensitivity analysis of flows involve the use of the shadowing trajectory. The state-of-the-art approach computes the shadowing trajectory by solving a least squares minimization problem, resulting in a space-time linear system of equations. The current paper computes the adjoint shadowing trajectory using the stabilized march, by specifying the adjoint boundary conditions instead of solving a minimization problem. This approach results in a space-time linear system that can be solved through a single backward substitution of order O(nu2) with nu being the dimension of the unstable subspace. It is proven to compute sensitivities that converge to the true sensitivity for large integration times and that the error in the sensitivity due to the discretization is of the order of the local truncation error of the scheme. The approach is numerically verified on the Lorentz 63 and Kuramoto-Sivasinsky equations.