Low-Order Modeling and Machine Learning in Fluid Dynamics: General III
ORAL · Z11 · ID: 3583309
Presentations
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Early detection of global instability via a large language model
ORAL
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Presenters
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Jun HUR
The Hong Kong University of Science and Technology
Authors
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Jun HUR
The Hong Kong University of Science and Technology
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Jungjin Park
The Hong Kong University of Science and Technology
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Zhijian YANG
The Hong Kong University of Science and Technology
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Bo YIN
The Hong Kong University of Science and Technology
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Larry K.B. Li
The Hong Kong University of Science and Technology
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Developing a data-driven model for unsteady prediction of the flow features using the attention-based approach
ORAL
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Presenters
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Hamid Karbasian
Southern Methodist University
Authors
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Hamid Karbasian
Southern Methodist University
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yassine hafiane
SMU
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Scale Aware Deep Learning for Precipitation Prediction with Hybrid Loss Swin Transformer U-Net
ORAL
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Presenters
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Jun Park
KAIST
Authors
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Jun Park
KAIST
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Changhoon Lee
Yonsei University
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Modeling convective heat transfer in a cavity flow using physics-informed neural networks
ORAL
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Presenters
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YI-TING HE
National Tsing Hua University
Authors
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YI-TING HE
National Tsing Hua University
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Ching Chang
National Tsing Hua University
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A Hybrid Reduced-Order Modeling Framework for Accelerating Physics-Informed Neural Networks in Modeling Flow Instabilities
ORAL
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Publication: No paper has been published yet. The manuscript for this work is in progress, and we plan to publish it in Physics of Fluids.
Presenters
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Sangeetha S
Indian Institute of Technology Madras
Authors
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Sangeetha S
Indian Institute of Technology Madras
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Vallabh Deogaonkar
Indian Institute of Technology Madras
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Arjun Jagannathan
Indian Institute of Technology, Madras
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Abhilash Somayajula
Indian Institute of Technology Madras
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Pipe flow reconstruction using a novel physics-informed machine-learning technique
ORAL
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Presenters
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Evan Yeremy
University of Alberta
Authors
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Evan Yeremy
University of Alberta
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Suyash Verma
Univ of Alberta
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Arman Hemmati
Univ of Alberta
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Coarse-to-fine variational inference with physics-informed deep learning for complex fluid motion estimation
ORAL
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Presenters
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Li Wei
Shanghai Jiao Tong University
Authors
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Li Wei
Shanghai Jiao Tong University
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Xiaoxian Guo
Shanghai Jiao Tong University
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Local Constraint Neural Operator for Discretization-Invariant Super-Resolution Turbulent Flow Prediction
ORAL
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Presenters
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Jungwon Heo
Yonsei University
Authors
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Jungwon Heo
Yonsei University
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Changhoon Lee
Yonsei University
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Towards Optimal Sensor Trajectory for Flow Reconstruction
ORAL
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Presenters
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Anand Karki
University of Waterloo
Authors
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Anand Karki
University of Waterloo
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Oleksandr Reshetar
University of Waterloo, Department of Engineering, University of Waterloo
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