Modeling Methods II: Speed-up, Stabilization, and Super-Resolution
ORAL · X29 · ID: 1765374
Presentations
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Implicit Neural Solver for Stable Surrogate Simulation of Fluid Dynamics
ORAL
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Presenters
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Deepak Akhare
University of Notre Dame
Authors
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Deepak Akhare
University of Notre Dame
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Pan Du
University of Notre Dame
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Tengfei Luo
University of Notre Dame
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Jian-Xun Wang
University of Notre Dame
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Machine-aided initial guesses for unstable periodic orbits
ORAL
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Presenters
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Pierre Beck
Ecole Polytechnique Fédérale de Lausanne
Authors
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Pierre Beck
Ecole Polytechnique Fédérale de Lausanne
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Jeremy P Parker
Ecole Polytechnique Federale de Lausanne
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Tobias M Schneider
Ecole Polytechnique Federale de Lausanne
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Eigen analysis of neural autoregressive models of multi-scale chaotic systems: Stability and error propagation
ORAL
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Publication: Ashesh Chattopadhyay and Pedram Hassanzadeh, Long-term instabilities of deep learning-based digital twins of the climate system: The cause and a solution, arXiv:2304.07029v1
Presenters
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Ashesh K Chattopadhyay
University of California, Santa Cruz
Authors
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Ashesh K Chattopadhyay
University of California, Santa Cruz
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Pedram Hassanzadeh
Rice University
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Harnessing the power of natural language processing techniques for multiscale turbulence simulation
ORAL
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Publication: https://arxiv.org/abs/2305.16564
Presenters
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Mrigank Dhingra
Virginia Tech
Authors
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Mrigank Dhingra
Virginia Tech
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Omer San
Oklahoma State University Stillwater
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Anne E Staples
Virginia Tech
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Abstract Withdrawn
ORAL Withdrawn
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MuRFiV-Net: A Multi-Resolution Finite-Volume Inspired Neural Network for Predicting Spatiotemporal Dynamics
ORAL
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Presenters
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Xin-yang Liu
University of Notre Dame
Authors
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Xin-yang Liu
University of Notre Dame
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Xiantao Fan
University of Notre Dame
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Jian-Xun Wang
University of Notre Dame
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Non-Intrusive Reduced Order Models with Neural PDEs: The Interpretability Challenge
ORAL
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Presenters
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Arvind T Mohan
Los Alamos National Laboratory
Authors
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Arvind T Mohan
Los Alamos National Laboratory
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Particle Dispersion in Indoor Environments: Can Super-resolution Autoencoders Revolutionize Air Quality Predictions?
ORAL
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Publication: 1. https://doi.org/10.1007/s11356-022-21579-y<br>2. https://doi.org/10.1016/j.buildenv.2022.109489 <br>3. https://doi.org/10.1016/j.enbuild.2023.112810<br>4. https://doi.org/10.1016/j.buildenv.2023.110048<br>5. https://doi.org/10.1080/19401493.2020.1812722
Presenters
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Adib Bazgir
University of Missouri
Authors
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Adib Bazgir
University of Missouri
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Hong Y Kek
Universiti Teknologi Malaysia
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Huiyi Tan
Universiti Teknologi Malaysia
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Yuwen Zhang
University of Missouri
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Keng Y Wong
Universiti Teknologi Malaysia
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Data-driven symmetry-aware low-dimensional models for predicting turbulent fluid flows
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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Alec Linot
University of California, Los Angeles
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Michael D Graham
University of Wisconsin - Madison
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Taylor series error correction network for super-resolution of discretized fluid solutions
ORAL
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Publication: [1] L E ̧ca, M Hoekstra, JF Beja Pedro, and JAC Falcao de Campos. On the characterization of grid density in grid refinement studies for discretization error estimation. International Journal for Numerical Methods in Fluids, 72(1):119–134, 201
Presenters
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Wenzhuo Xu
Carnegie Mellon University
Authors
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Wenzhuo Xu
Carnegie Mellon University
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Christopher McComb
Carnegie Mellon University
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Noelia Grande Gutiérrez
Carnegie Mellon University
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A Differentiable Hybrid Neural Solver for Efficient Simulation of Cavitating Flows
ORAL
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Presenters
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Bo Zhang
University of Notre Dame
Authors
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Bo Zhang
University of Notre Dame
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Xiantao Fan
University of Notre Dame
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Jian-Xun Wang
University of Notre Dame
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Neural operator-based super-fidelity: A warm-start approach for accelerating steady-state fluid flow simulations
ORAL
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Presenters
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Xuhui Zhou
Virginia Tech
Authors
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Xuhui Zhou
Virginia Tech
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Jiequn Han
Flatiron Institute
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Muhammad Irfan Zafar
Virginia Tech
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Christopher Roy
virginia tech
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Heng Xiao
University of Stuttgart
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