The adjoint method is used for high-fidelity aerodynamic shape optimization and is an
efficient approach for computing the derivatives of a function of interest with respect to a …

Forecasting high-dimensional dynamical systems is a fundamental challenge in various
fields, such as geosciences and engineering. Neural Ordinary Differential Equations …

This paper develops the Non-Intrusive Least Squares Shadowing (NILSS) method, which
computes the sensitivity for long-time averaged objectives in chaotic dynamical systems. In …

Learning dynamics from dissipative chaotic systems is notoriously difficult due to their
inherent instability, as formalized by their positive Lyapunov exponents, which exponentially …

The simulation of turbulent flows requires high spatial resolution in potentially a priori
unknown, solution-dependent locations. To achieve adaptive refinement of the mesh, we …

This paper presents a discrete adjoint version of the recently developed non-intrusive least
squares shadowing (NILSS) algorithm, which circumvents the instability that conventional …

In this paper we demonstrate the ability to perform gradient-free aerodynamic shape
optimization using Large Eddy Simulation (LES) with the Mesh Adaptive Direct Search …

This paper uses compressible flow simulation to analyse the hyperbolicity, shadowing
directions and sensitivities of a weakly turbulent three-dimensional cylinder flow at Reynolds …

Sensitivity analysis methods are important tools for research and design with simulations.
Many important simulations exhibit chaotic dynamics, including scale-resolving turbulent …

This study delves into the discrete adjoint method, a powerful tool in high-fidelity
aerodynamic shape optimization that efficiently computes derivatives of a target function for …