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Sparse Sensing and Modal Decomposition for Unsteady Fluid Flows
This work explores data-driven methods, including sparse sampling, modal decomposition and machine learning techniques, for high-dimensional systems in fluid dynamics. Fluid flows are characterized by their nonlinearity, multi-scale structures and unsteady behaviors. Understanding the patterns and their evolving dynamics is ...
Modeling and predicting response to ankle exoskeletons
Ankle exoskeletons are designed and personalized to enhance mobility in unimpaired adults and improve gait in individuals with motor impairments. Ankle exoskeletons are challenging to prescribe and optimize for an individual, resulting in inconsistent intervention outcomes. Quantifying and predicting changes in kinematics and ...
Machine learning for dynamical models of human movement
Data-driven dynamical modeling is an emerging and powerful tool for analyzing, predicting, and controlling complex systems in engineering and physical sciences. Traditionally, modeling of dynamical systems uses mathematical approaches like differential equations; modern approaches leverage advances in machine learning to ...