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We propose a novel differentiable vortex particle (DVP) method to infer and predict fluid dynamics from a single video.
Convergence of vortex methods for euler’s equations. ii
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Anthony Leonard · 1980
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J Thomas Beale and Andrew Majda · 1985
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Contributions to vortex particle methods for the computation of three-dimensional incompressible unsteady flows
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Vortex methods: theory and practice , volume 8
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Visual simulation of smoke
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Flowfixer: Using bfecc for fluid simulation
ByungMoon Kim, Yingjie Liu, Ignacio Llamas, and Jaroslaw R Rossignac · 2005
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Vortex fluid for gaseous phenomena
Sang Il Park and Myoung Jun Kim · 2005
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A vortex particle method for smoke, water and explosions
Andrew Selle, Nick Rasmussen, and Ronald Fedkiw · 2005
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Josh Bongard and Hod Lipson · 2007
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Distilling free-form natural laws from experimental data
Michael Schmidt and Hod Lipson · 2009
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Dynamic mode decomposition of numerical and experimental data
Peter J Schmid · 2010
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Filament-based smoke with vortex shedding and variational reconnection
Steffen Weißmann and Ulrich Pinkall · 2010
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Linear-time smoke animation with vortex sheet meshes
Tyson Brochu, Todd Keeler, and Robert Bridson · 2012
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Data-driven fluid simulations using regression forests
L’ubor Ladickỳ, SoHyeon Jeong, Barbara Solenthaler, Marc Pollefeys, and Markus Gross · 2015
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U-net: Convolutional networks for biomedical image segmentation, 2015
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Interaction networks for learning about objects, relations and physics
Peter Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, et al · 2016
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Discovering governing equations from data by sparse identification of nonlinear dynamical systems
Steven L Brunton, Joshua L Proctor, and J Nathan Kutz · 2016
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A compositional object-based approach to learning physical dynamics
Michael B Chang, Tomer Ullman, Antonio Torralba, and Joshua B Tenenbaum · 2016
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Convolutional neural networks for steady flow approximation
Xiaoxiao Guo, Wei Li, and Francesco Iorio · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Maziar Raissi, Paris Perdikaris, and George E Karniadakis · 2019
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Lagrangian fluid simulation with continuous convolutions
Benjamin Ummenhofer, Lukas Prantl, Nils Thuerey, and Vladlen Koltun · 2019
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Latent space physics: Towards learning the temporal evolution of fluid flow
Steffen Wiewel, Moritz Becher, and Nils Thuerey · 2019
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Difftaichi: Differentiable programming for physical simulation
Yuanming Hu, Luke Anderson, Tzu-Mao Li, Qi Sun, Nathan Carr, Jonathan Ragan-Kelley, and Frédo Durand · 2020
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Lagrangian neural style transfer for fluids
Byungsoo Kim, Vinicius C Azevedo, Markus Gross, and Barbara Solenthaler · 2020
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Justin Johnson, Alexandre Alahi, and Li Fei-Fei · 2016
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Dynamic mode decomposition: data-driven modeling of complex systems
J Nathan Kutz, Steven L Brunton, Bingni W Brunton, and Joshua L Proctor · 2016
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Data-driven synthesis of smoke flows with cnn-based feature descriptors
Mengyu Chu and Nils Thuerey · 2017
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Machine learning of linear differential equations using gaussian processes
Maziar Raissi, Paris Perdikaris, and George Em Karniadakis · 2017
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Data-driven discovery of partial differential equations
Samuel H Rudy, Steven L Brunton, Joshua L Proctor, and J Nathan Kutz · 2017
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Accelerating eulerian fluid simulation with convolutional networks
Jonathan Tompson, Kristofer Schlachter, Pablo Sprechmann, and Ken Perlin · 2017
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Visual interaction networks: Learning a physics simulator from video
Nicholas Watters, Daniel Zoran, Theophane Weber, Peter Battaglia, Razvan Pascanu, and Andrea Tacchetti · 2017
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Tobias Pfaff, Meire Fortunato, Alvaro Sanchez-Gonzalez, and Peter W Battaglia · 2020
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Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
Maziar Raissi, Alireza Yazdani, and George Em Karniadakis · 2020
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Learning to simulate complex physics with graph networks
Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff, Rex Ying, Jure Leskovec, and Peter Battaglia · 2020
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Implicit neural representations with periodic activation functions
Vincent Sitzmann, Julien Martel, Alexander Bergman, David Lindell, and Gordon Wetzstein · 2020
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Neural vortex method: from finite lagrangian particles to infinite dimensional eulerian dynamics
Shiying Xiong, Xingzhe He, Yunjin Tong, and Bo Zhu · 2020
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Physics-informed generative adversarial networks for stochastic differential equations
Liu Yang, Dongkun Zhang, and George Em Karniadakis · 2020
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Nsfnets (navier-stokes flow nets): Physics-informed neural networks for the incompressible navier-stokes equations
Xiaowei Jin, Shengze Cai, Hui Li, and George Em Karniadakis · 2021
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A review of vortex methods and their applications: From creation to recent advances
Chloé Mimeau and Iraj Mortazavi · 2021
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Physics informed neural fields for smoke reconstruction with sparse data
Mengyu Chu, Lingjie Liu, Quan Zheng, Erik Franz, Hans-Peter Seidel, Christian Theobalt, and Rhaleb Zayer · 2022
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Neurofluid: Fluid dynamics grounding with particle-driven neural radiance fields
Shanyan Guan, Huayu Deng, Yunbo Wang, and Xiaokang Yang · 2022
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Learning to estimate and refine fluid motion with physical dynamics
Mingrui Zhang, Jianhong Wang, James Tlhomole, and Matthew D Piggott · 2022
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