Dynamic mode decomposition (DMD) is a method for extracting dynamic information from numerical and experimental flow data. It identifies coherent structures in flow fields by decomposing them into dynamic modes, which can be interpreted as a generalization of global stability modes. DMD is particularly useful for analyzing both numerical simulations and experimental data, as it does not require knowledge of the underlying system matrix. The method is based on snapshots of the flow field and can be applied to both linear and nonlinear flows. DMD has been validated on various flow cases, including plane channel flow, flow over a two-dimensional cavity, wake flow behind a flexible membrane, and a jet passing between two cylinders. The method is able to capture the dominant dynamic behavior of the flow and can be used to project large-scale problems onto a reduced set of degrees of freedom. DMD has been shown to be effective in identifying coherent structures and has been compared to other decomposition techniques such as proper orthogonal decomposition (POD). The method is also applicable to subdomains of the flow field, allowing for the analysis of localized instability phenomena. DMD has been used to analyze both temporal and spatial flow data, and its results have been validated against experimental measurements. The method is particularly useful for analyzing flows with complex dynamics, as it can capture the dominant modes of behavior and provide insights into the underlying physical mechanisms. DMD has been shown to be a powerful tool for analyzing flow data and has the potential to be applied to a wide range of fluid dynamics problems.Dynamic mode decomposition (DMD) is a method for extracting dynamic information from numerical and experimental flow data. It identifies coherent structures in flow fields by decomposing them into dynamic modes, which can be interpreted as a generalization of global stability modes. DMD is particularly useful for analyzing both numerical simulations and experimental data, as it does not require knowledge of the underlying system matrix. The method is based on snapshots of the flow field and can be applied to both linear and nonlinear flows. DMD has been validated on various flow cases, including plane channel flow, flow over a two-dimensional cavity, wake flow behind a flexible membrane, and a jet passing between two cylinders. The method is able to capture the dominant dynamic behavior of the flow and can be used to project large-scale problems onto a reduced set of degrees of freedom. DMD has been shown to be effective in identifying coherent structures and has been compared to other decomposition techniques such as proper orthogonal decomposition (POD). The method is also applicable to subdomains of the flow field, allowing for the analysis of localized instability phenomena. DMD has been used to analyze both temporal and spatial flow data, and its results have been validated against experimental measurements. The method is particularly useful for analyzing flows with complex dynamics, as it can capture the dominant modes of behavior and provide insights into the underlying physical mechanisms. DMD has been shown to be a powerful tool for analyzing flow data and has the potential to be applied to a wide range of fluid dynamics problems.