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SUMMARY:MEAM Seminar: "Operator Inference: Bridging Model Reduction and Scientific Machine Learning"
DESCRIPTION:Model reduction methods have grown from the computational science community\, with a focus on reducing high-dimensional models that arise from physics-based modeling\, whereas machine learning has grown from the computer science community\, with a focus on creating expressive models from black-box data streams. Yet recent years have seen an increased blending of the two perspectives and a recognition of the associated opportunities. This talk presents our work in operator inference\, where we learn effective reduced-order operators directly from data. The physical governing equations define the form of the model we should seek to learn. Thus\, rather than learn a generic approximation with weak enforcement of the physics\, we learn low-dimensional operators whose structure is defined by the physics. This perspective provides new opportunities to learn from data through the lens of physics-based models and contributes to the foundations of Scientific Machine Learning\, yielding a new class of flexible data-driven methods that support high-consequence decision-making under uncertainty for physical systems.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-operator-inference-bridging-model-reduction-and-scientific-machine-learning/
LOCATION:Zoom – Email MEAM for Link\, peterlit@seas.upenn.edu
CATEGORIES:Seminar
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
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