Low-Order Modeling and Machine Learning in Fluid Dynamics: Methods II
ORAL · J15 · ID: 2665276
Presentations
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Stochastic Generation of Lagrangian Turbulent Signals by Conditional Generative Diffusion Models
ORAL
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Publication: Li, T., Biferale, L., Bonaccorso, F., Scarpolini, M. A., & Buzzicotti, M. (2024). Synthetic Lagrangian turbulence by generative diffusion models. Nature Machine Intelligence, 1-11.
Presenters
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Michele Buzzicotti
University of Rome Tor Vergata and INFN, INFN-Rome
Authors
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Michele Buzzicotti
University of Rome Tor Vergata and INFN, INFN-Rome
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Luca Biferale
University of Rome Tor Vergata and INFN
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Tianyi Li
University of Rome Tor Vergata
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Fabio Bonaccorso
University of Rome Tor Vergata
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Martino Andrea Scarpolini
Gran Sasso Science Institute
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Assimilating Shear Stress Distributions from Sparse Measurement Data and Flow Visualizations Using Deep Neural Networks
ORAL
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Publication: - Rohlfs, L., et al. "TUBflow-An Open Source Application for Digital Postprocessing of Oil Film Visualizations in Wind Tunnels." (AIAA Aviation Forum 2024)
Presenters
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Lennart Rohlfs
TU Berlin
Authors
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Lennart Rohlfs
TU Berlin
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Julien Weiss
TU Berlin
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Denoising and super-resolution of Flow Data by Physics-Informed Markov Random Fields
ORAL
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Presenters
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Cem Gormezano
University of California Berkeley
Authors
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Cem Gormezano
University of California Berkeley
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Shawn C Shadden
University of California, Berkeley
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Gaussian-process-augmented projection-based model order reduction for mitigating the Kolmogorov barrier to reducibility
ORAL
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Presenters
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Carlos Gonzalez Hernandez
Stanford University
Authors
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Carlos Gonzalez Hernandez
Stanford University
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Carlos Gonzalez Hernandez
Stanford University
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Carlos Gonzalez Hernandez
Stanford University
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Frequency-domain nonlinear model reduction using SPOD modes
ORAL
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Presenters
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Peter Keaton Frame
University of Michigan
Authors
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Peter Keaton Frame
University of Michigan
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Aaron Towne
University of Michigan
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