Adjoint Sensitivities of Chaotic Flows without Adjoint Solvers: A Data-Driven Approach

arXiv:2404.12315v1 Announce Type: new
Abstract: In one calculation, adjoint sensitivity analysis provides the gradient of a quantity of interest with respect to all system's parameters. Conventionally, adjoint solvers need to be implemented by differentiating computational models, which can be a cumbersome task and is code-specific. To propose an adjoint solver that is not code-specific, we develop a data-driven strategy. We demonstrate its application on the computation of gradients of long-time averages of chaotic flows. First, we deploy a parameter-aware echo …

abstract analysis arxiv code computational cs.lg data data-driven gradient nlin.cd parameters sensitivity type