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Description: Reduced-order models are often obtained by projection onto a subspace; standard least squares in linear spaces is a ... Fluid phenomena are ubiquitous to our world experience: winds swooshing through trembling leaves, turbulent water streams ... Description: In this talk, we will investigate various approaches to modeling dynamical systems from
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DDPS | 'Probabilistic methods for data-driven reduced-order modeling'
DDPS | Modeling and controlling turbulent flows through deep learning
DDPS | The Nexus of Machine Learning, Physics-based Modeling, and Uncertainty Quantification
DDPS | Data-Driven Closure Modeling Using Derivative-free Kalman Methods
DDPS | Generative Models for Data Assimilation in Subsurface Flow
DDPS | Generative Machine Learning Approaches for Data-Driven Modeling and Reductions
DDPS | Data-driven modeling of dynamical systems: A systems theoretic perspective
DDPS | “Data-driven techniques for analysis of turbulent flows”
DDPS | Data-driven constitutive updates: from model-free poroelasticity to level set plasticity
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Last Updated: June 14, 2026
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