Adjoint shadowing directions in hyperbolic systems for sensitivity analysis
Provides a theoretical foundation for efficient adjoint sensitivity methods in chaotic systems, relevant for researchers in dynamical systems and optimization.
The paper defines adjoint shadowing directions for hyperbolic systems and proves their unique existence, enabling adjoint sensitivity analysis for long-time-averaged objectives like NILSAS.
For hyperbolic diffeomorphisms, we define adjoint shadowing directions as a bounded inhomogeneous adjoint solution whose initial condition has zero component in the unstable adjoint direction. For hyperbolic flows, we define adjoint shadowing directions similarly, with the additional requirement that the average of its inner-product with the trajectory direction is zero. In both cases, we show unique existence of adjoint shadowing directions, and how they can be used for adjoint sensitivity analysis. Our work set a theoretical foundation for efficient adjoint sensitivity methods for long-time-averaged objectives such as NILSAS.