ModelFLOWs is a research group led by Full Professor Soledad Le Clainche. The group is based in the Department of Applied Mathematics at the School of Aerospace Engineering, Universidad Politécnica de Madrid (UPM). Our team employs diverse data-driven methods—such as modal decompositions, machine learning architectures, and data assimilation tools—to generate hybrid reduced order models grounded in physics. These models are designed to study the physics of complex dynamical systems, reconstruct and repair databases, and perform temporal forecasting. Because these models are physics-informed, they are highly robust and exhibit strong generalization capabilities. They have been successfully applied to a variety of complex flows (turbulent, reactive, etc.) and other non-linear dynamical systems. Additionally, the ModelFLOWs group has extensive expertise in computational fluid dynamics (CFD), flow control, and the development of novel tools and methodologies for data analysis.
Groups Members
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