Low-Order Modeling and Machine Learning in Fluid Dynamics: Design
ORAL · J12 · ID: 2665156
Presentations
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Data-Driven Automotive Aerodynamic Shape Optimization
ORAL
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Publication: Aerodynamics-Guided Machine Learning for Design Optimization of Electric Vehicles
Presenters
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Jonathan Quang Tran
Department of Mechanical and Aerospace Engineering, University of California, Los Angeles
Authors
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Jonathan Quang Tran
Department of Mechanical and Aerospace Engineering, University of California, Los Angeles
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Kai Fukami
Department of Mechanical and Aerospace Engineering, University of California, Los Angeles
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Kenta Inada
BEV Automobile Development Unit, Honda Motor Co., Ltd.
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Daisuke Umehara
BEV Automobile Development Unit, Honda Motor Co., Ltd.
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Yoshimichi Ono
BEV Automobile Development Unit, Honda Motor Co., Ltd.
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Kenta Ogawa
BEV Automobile Development Unit, Honda Motor Co., Ltd.
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Kunihiko Taira
University of California, Los Angeles, Department of Mechanical and Aerospace Engineering, University of California, Los Angeles
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A systematic dataset generation technique for data-driven automotive drag prediction
ORAL
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Presenters
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Mark Benjamin
Department of Mechanical Engineering, Stanford University
Authors
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Mark Benjamin
Department of Mechanical Engineering, Stanford University
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Gianluca Iaccarino
Stanford University
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Abstract Withdrawn
ORAL Withdrawn
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ABSTRACT WITHDRAWN
COFFEE_KLATCH
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Bayesian optimal design accelerates discovery of material properties from bubble dynamics
ORAL
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Presenters
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Tianyi Chu
University of California, San Diego, Georgia Institute of Technology
Authors
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Tianyi Chu
University of California, San Diego, Georgia Institute of Technology
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Jonathan Estrada
University of Michigan
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Spencer H. Bryngelson
Georgia Institute of Technology
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