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Reduced-order modeling of fluid flows

Welcome to the fourth video in our series on Data-Driven Models for Unsteady Fluid Flows. In this video, we focus on Reduced-Order Modeling (ROM) techniques that are crucial for describing the evolution of fluid flows in a computationally efficient manner. We'll delve into Galerkin projection, linear regression, Sparse Identification of Nonlinear Dynamics (SINDy), Eigensystem realization, phase-based models, and network-oscillator models. 🕒 Timestamps: 0:00 - Introduction 0:23 - Galerkin Projection in ROMs 09:21 - Linear Regression for Fluid Flow Modeling 12:55 - Sparse Identification of Nonlinear Dynamics (SINDy) 19:52 - Eigensystem Realization Algorithm 25:25 - Phase-Based Models for Fluid Flow Evolution 28:52 - Network-Oscillator Models in ROMs 32:51 - Quick recap This lecture series was recorded as part of the NSF AI Institute of Dynamic Systems. Check out dynamicsai.org/edu for more educational resources. #FluidDynamics #ReducedOrderModeling #ROMs #GalerkinProjection #SINDy #DataDrivenModels