Nonlinear Dynamics: Model Reduction & Turbulence IV
ORAL · M31 · ID: 22883
Presentations
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Development of closures for coarse-scale modeling of multiphase and free surface flows using machine learning.
ORAL
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Presenters
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Cristina P Martin Linares
- Department of Mechanical Engineering, Whiting School of Engineering, Johns Hopkins University
Authors
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Cristina P Martin Linares
- Department of Mechanical Engineering, Whiting School of Engineering, Johns Hopkins University
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Tom Bertalan
- Department of Mechanical Engineering, Massachusetts Institute of Technology
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Eleni Koronaki
- Department of Engineering, Faculty of Science, Technology and Medicine, Université du Luxembourg
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Jiacai Lu
- Johns Hopkins University
- Johns Hopkins University Department of Mechanical Engineering
- Department of Mechanical Engineering, Whiting School of Engineering, Johns Hopkins University
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Gretar Tryggvason
- Johns Hopkins University
- Johns Hopkins
- Department of Mechanical Engineering, Whiting School of Engineering, Johns Hopkins University
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Ioannis G Kevrekidis
- Department of Chemical and Biomolecular Engineering, Whiting School of Engineering, Johns Hopkins University
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Analyses of the Differentiable-programming Paradigm for Learning Physics-constrained Surrogate Models
ORAL
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Presenters
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Arvind T Mohan
- Los Alamos National Laboratory
- Computational Physics and Methods Group, Los Alamos National Laboratory
- Los Alamos National Laboratory, Los Alamos, NM, USA
Authors
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Divya Sri Praturi
- Chair of Fluid Dynamics, TU Darmstadt
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Arvind T Mohan
- Los Alamos National Laboratory
- Computational Physics and Methods Group, Los Alamos National Laboratory
- Los Alamos National Laboratory, Los Alamos, NM, USA
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Frequency-time analysis of turbulent flows using spectral POD
ORAL
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Publication: A. Nekkanti, and O. T. Schmidt. "Frequency-time analysis, low-rank reconstruction and denoising of turbulent flows using SPOD." arXiv preprint arXiv:2011.03644 (2020).
Presenters
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Akhil Nekkanti
- University of California San Diego
- University of California, San Diego
Authors
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Akhil Nekkanti
- University of California San Diego
- University of California, San Diego
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Oliver T. T Schmidt
- Mechanical and Aerospace Engineering, University of California, San Diego
- University of California, San Diego
- University of California San Diego
- UC San Diego
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Data-driven coarse graining of many-body systems
ORAL
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Presenters
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Zachary G Nicolaou
- University of Washington
Authors
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Zachary G Nicolaou
- University of Washington
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Matthew Kafker
- University of Washington
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Steven L Brunton
- University of Washington
- University of Washington, Seattle
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Nathan Kutz
- University of Washington, Seattle
- University of Washington
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Optimizing oblique projections for nonlinear systems using trajectories
ORAL
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Publication: Otto, Samuel E., Alberto Padovan, and Clarence W. Rowley. "Optimizing Oblique Projections for Nonlinear Systems using Trajectories." arXiv preprint arXiv:2106.01211 (2021).
Presenters
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Samuel E Otto
- Princeton University
Authors
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Samuel E Otto
- Princeton University
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Alberto Padovan
- Princeton University
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Clarence W Rowley
- Princeton University
- Princeton
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Neural Implicit Flow: A mesh-agnostic representation paradigm for spatio-temporal fields
ORAL
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Presenters
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Shaowu Pan
- University of Washington, Seattle
Authors
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Shaowu Pan
- University of Washington, Seattle
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Steven L Brunton
- University of Washington
- University of Washington, Seattle
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Nathan Kutz
- University of Washington, Seattle
- University of Washington
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Physics-guided machine learning for surrogate modeling in fluid mechanics
ORAL
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Publication: 1. Pawar, Suraj, et al. "Physics guided machine learning using simplified theories." Physics of Fluids 33.1 (2021): 011701.
2. Pawar, Suraj, et al. "Model fusion with physics-guided machine learning: Projection-based reduced-order modeling." Physics of Fluids 33.6 (2021): 067123.Presenters
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Suraj A Pawar
- Oklahoma State University-Stillwater
Authors
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Suraj A Pawar
- Oklahoma State University-Stillwater
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Omer San
- Oklahoma State University-Stillwater
- Oklahoma State University
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Adil Rasheed
- Norwegian University of Science and Technology
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Data-driven estimation of inertial manifold dimension for chaotic Kolmogorov flow and time evolution on the manifold
ORAL
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Presenters
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Carlos E Perez De Jesus
- University of Wisconsin - Madison
Authors
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Carlos E Perez De Jesus
- University of Wisconsin - Madison
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Michael D Graham
- University of Wisconsin - Madison
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Promoting global stability in data-driven models of quadratic nonlinear dynamics
ORAL
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Publication: https://arxiv.org/abs/2105.01843
Presenters
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Alan Kaptanoglu
- University of Washington
Authors
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Alan Kaptanoglu
- University of Washington
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Jared Callaham
- University of Washington
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Christopher J Hansen
- University of Washington
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Aleksandr Aravkin
- University of Washington
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Steven L Brunton
- University of Washington
- University of Washington, Seattle
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Modeling chaotic spatiotemporal dynamics with a minimal representation using Neural ODEs
ORAL
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Presenters
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Alec Linot
- University of Wisconsin - Madison
Authors
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Alec Linot
- University of Wisconsin - Madison
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Michael D Graham
- University of Wisconsin - Madison
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