Fetching the paper…
Reading the bibliography…
We present a generative AI algorithm for addressing the pressing task of fast, accurate, and robust statistical computation of three-dimensional turbulent fluid flows.
Mechanism of the production of small eddies from large ones
G. I. Taylor and A. E. Green · 1937
Earlier work this paper cites.
Fluid Mechanics, 2nd edition
L. D. Landau and E. M. Lipschitz · 1987
Earlier work this paper cites.
A second-order projection method for the incompressible Navier-Stokes equations
J.B. Bell, P. Collela, and H. M. Glaz · 1989
Earlier work this paper cites.
Convergence of spectral methods for nonlinear conservation laws
Eitan Tadmor · 1989
Earlier work this paper cites.
Numerical methods for conservation laws
Randall J LeVeque · 1992
Earlier work this paper cites.
A comparison of shear- and buoyancy-driven planetary boundary layer flows
Chin-Hoh Moeng and Peter P. Sullivan · 1994
Earlier work this paper cites.
Turbulence: The Legacy of A.N. Kolmogorov
Uriel Frisch · 1995
Earlier work this paper cites.
An analysis of numerical errors in large eddy simulations of turbulence
S. Ghoshal · 1996
Earlier work this paper cites.
On the computation of crytalline microstructure
M. Luskin · 1996
Earlier work this paper cites.
Navier-Stokes Equations and Turbulence
Ciprian Foias, Oscar Manley, Ricardo Rosa, and Roger Temam · 2001
Earlier work this paper cites.
Scaling Laws for Neural Language Models, January 2020
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2001
Earlier work this paper cites.
Vorticity and Incompressible Flow
Andrew J. Majda and Andrea L. Bertozzi · 2001
Earlier work this paper cites.
Turbulent flows
Stephen B Pope · 2001
Earlier work this paper cites.
Hyperbolic conservation laws in continuum physics
Constantine M Dafermos and Constantine M Dafermos · 2005
Earlier work this paper cites.
Large Eddy Simulations for Incompressible Flows
Pierre Saguat · 2006
Earlier work this paper cites.
Asymptotic and numerical homogenization
B. Engquist and P. E. Souganidis · 2008
Earlier work this paper cites.
Thermodynamic consistency of the anelastic approximation for a moist atmosphere
Olivier Pauluis · 2008
Earlier work this paper cites.
Learning Mesh-Based Simulation with Graph Networks, June 2021
Tobias Pfaff, Meire Fortunato, Alvaro Sanchez-Gonzalez, and Peter W. Battaglia · 2010
Earlier work this paper cites.
Inverse problems: a Bayesian perspective
Andrew M Stuart · 2010
Earlier work this paper cites.
The Effect of Mesh Resolution on Convective Boundary Layer Statistics and Structures Generated by Large-Eddy Simulation
Peter P. Sullivan and Edward G. Patton · 2011
Earlier work this paper cites.
A connection between score matching and denoising autoencoders
Pascal Vincent · 2011
Earlier work this paper cites.
Multi-level Monte Carlo finite volume methods for nonlinear systems of conservation laws in multi-dimensions
S. Mishra, Ch. Schwab, and J. Šukys · 2012
Earlier work this paper cites.
On the difficulty of training recurrent neural networks
Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio · 2013
Earlier work this paper cites.
Uncertainty quantification in computational fluid dynamics
H Bijl, D Lucor, S Mishra, and C (Eds). Schwab · 2014
Earlier work this paper cites.
The Feynman lectures on physics, Vol. I: The new millennium edition: mainly mechanics, radiation, and heat
Richard P Feynman, Robert B Leighton, and Matthew Sands · 2015
Earlier work this paper cites.
Large-eddy simulation in an anelastic framework with closed water and entropy balances
Kyle G. Pressel, Colleen M. Kaul, Tapio Schneider, Zhihong Tan, and Siddhartha Mishra · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Earlier work this paper cites.
On the computation of measure-valued solutions
U. S. Fjordholm, S. Mishra, and E. Tadmor · 2016
Earlier work this paper cites.
Construction of approximate entropy measure-valued solutions for hyperbolic systems of conservation laws
Ulrik S Fjordholm, Roger Käppeli, Siddhartha Mishra, and Eitan Tadmor · 2017
Cited alongside, same era.
Statistical solutions of hyperbolic conservation laws: Foundations
Ulrik S. Fjordholm, Samuel Lanthaler, and Siddhartha Mishra · 2017
Cited alongside, same era.
Numerics and subgrid-scale modeling in large eddy simulations of stratocumulus clouds
Kyle G. Pressel, Siddhartha Mishra, Tapio Schneider, Colleen M. Kaul, and Zhihong Tan · 2017
Cited alongside, same era.
Earth systemmodeling
T. Schneider, S. Lan, A. Stuart, and J. Teixeira · 2017
Cited alongside, same era.
Climate goals and computing the future of clouds
Tapio Schneider, João Teixeira, Christopher S. Bretherton, Florent Brient, Kyle G. Pressel, Christoph Schär, and A. Pier Siebesma · 2017
Cited alongside, same era.
Numerical methods for conservation laws: From analysis to algorithms
Elucidating the design space of diffusion-based generative models
Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine · 2022
Later among the works it cites.
On bayesian data assimilation for pdes with ill-posed forward problems
S Lanthaler, S Mishra, and F Weber · 2022
Later among the works it cites.
High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
Later among the works it cites.
Photorealistic text-to-image diffusion models with deep language understanding
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al · 2022
Later among the works it cites.
Representation equivalent neural operators: a framework for alias-free operator learning
F. Bartolucci, E. de Bézenac, B Raonic, R Molinaro, S Mishra, and R Alaifari · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
J. S. Hesthaven · 2018
Cited alongside, same era.
Axial attention in multidimensional transformers
Jonathan Ho, Nal Kalchbrenner, Dirk Weissenborn, and Tim Salimans · 2019
Cited alongside, same era.
Axial attention in multidimensional transformers, 2019
Jonathan Ho, Nal Kalchbrenner, Dirk Weissenborn, and Tim Salimans · 2019
Cited alongside, same era.
On the spectral bias of neural networks, 2019
Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler, Min Lin, Fred A. Hamprecht, Yoshua Bengio, and Aaron Courville · 2019
Cited alongside, same era.
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
M. Raissi, P. Perdikaris, and G. E. Karniadakis · 2019
Cited alongside, same era.
Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
Cited alongside, same era.
Optimal machine intelligence at the edge of chaos, 2020
Ling Feng, Lin Zhang, and Choy Heng Lai · 2020
Cited alongside, same era.
Convolutional neural operators
Bogdan Raonić, Roberto Molinaro, Tobias Rohner, Siddhartha Mishra, and Emmanuel de Bezenac · 2023
Later among the works it cites.
Score-based data assimilation
François Rozet and Gilles Louppe · 2023
Later among the works it cites.
Debias coarsely, sample conditionally: Statistical downscaling through optimal transport and probabilistic diffusion models
Zhong Yi Wan, Ricardo Baptista, Anudhyan Boral, Yi-Fan Chen, John Anderson, Fei Sha, and Leonardo Zepeda-Núñez · 2023
Later among the works it cites.
A denoising diffusion model for fluid field prediction
Gefan Yang and Stefan Sommer · 2023
Later among the works it cites.
Lumiere: A space-time diffusion model for video generation
Omer Bar-Tal, Hila Chefer, Omer Tov, Charles Herrmann, Roni Paiss, Shiran Zada, Ariel Ephrat, Junhwa Hur, Yuanzhen Li, Tomer Michaeli, et al · 2024
Closest in time.
On some limitations of current machine learning weather prediction models
Massimo Bonavita · 2024
Closest in time.
Video generation models as world simulators
Tim Brooks, Bill Peebles, Connor Holmes, Will DePue, Yufei Guo, Li Jing, David Schnurr, Joe Taylor, Troy Luhman, Eric Luhman, Clarence Ng, Ricky Wang, and Aditya Ramesh · 2024
Closest in time.
Conditional score-based diffusion models for solving inverse problems in mechanics
Agnimitra Dasgupta, Harisankar Ramaswamy, Javier Murgoitio Esandi, Ken Foo, Runze Li, Qifa Zhou, Brendan Kennedy, and Assad Oberai · 2024
Closest in time.
Bayesian conditional diffusion models for versatile spatiotemporal turbulence generation
Han Gao, Xu Han, Xiantao Fan, Luning Sun, Li-Ping Liu, Lian Duan, and Jian-Xun Wang · 2024
Closest in time.
Generative learning for forecasting the dynamics of complex systems
Han Gao, Sebastian Kaltenbach, and Petros Koumoutsakos · 2024
Closest in time.
Han Gao, Sebastian Kaltenbach, and Petros Koumoutsakos · 2024
Closest in time.
Poseidon: Efficient foundation models for pdes, 2024
Maximilian Herde, Bogdan Raonić, Tobias Rohner, Roger Käppeli, Roberto Molinaro, Emmanuel de Bézenac, and Siddhartha Mishra · 2024
Closest in time.
Advances in Computational Process Engineering using Lattice Boltzmann Methods on High Performance Computers
Adrian Kummerländer, Fedor Bukreev, Simon F. R. Berg, Marcio Dorn, and Mathias J. Krause · 2024
Closest in time.
Optimization of Single Node Load Balancing for Lattice Boltzmann Methods on Heterogeneous High Performance Computers
Adrian Kummerländer, Fedor Bukreev, Dennis Teutscher, Marcio Dorn, and Mathias J. Krause · 2024
Closest in time.
OpenLB Release 1.7: Open Source Lattice Boltzmann Code, February 2024
Adrian Kummerländer, Tim Bingert, Fedor Bukreev, Luiz Eduardo Czelusniak, Davide Dapelo, Nicolas Hafen, Marc Heinzelmann, Shota Ito, Julius Jeßberger, Halim Kusumaatmaja, Jan E. Marquardt, Michael Rennick, Tim Pertzel, František Prinz, Martin Sadric, Maximilian Schecher, Stephan Simonis, Pascal Sitter, Dennis Teutscher, Mingliang Zhong, and Mathias J. Krause · 2024
Closest in time.
Generative emulation of weather forecast ensembles with diffusion models
Lizao Li, Robert Carver, Ignacio Lopez-Gomez, Fei Sha, and John Anderson · 2024
Closest in time.
Residual diffusion modeling for km-scale atmospheric downscaling, 2024
Morteza Mardani, Noah Brenowitz, Yair Cohen, Jaideep Pathak, Chieh-Yu Chen, Cheng-Chin Liu, Arash Vahdat, Karthik Kashinath, Jan Kautz, and Mike Pritchard · 2024
Closest in time.
Vivek Oommen, Aniruddha Bora, Zhen Zhang, and George Em Karniadakis · 2024
Closest in time.
Gencast: Diffusion-based ensemble forecasting for medium-range weather, 2024
Ilan Price, Alvaro Sanchez-Gonzalez, Ferran Alet, Tom R. Andersson, Andrew El-Kadi, Dominic Masters, Timo Ewalds, Jacklynn Stott, Shakir Mohamed, Peter Battaglia, Remi Lam, and Matthew Willson · 2024
Closest in time.
Efficient computation of large-scale statistical solutions to incompressible fluid flows
Tobias Rohner and Siddhartha Mishra · 2024
Closest in time.
Computing statistical Navier–Stokes solutions
S. Simonis and S. Mishra · 2024
Closest in time.
Score-based diffusion models via stochastic differential equations – a technical tutorial, 2024
Wenpin Tang and Hanyang Zhao · 2024
Closest in time.
Back-projection diffusion: Solving the wideband inverse scattering problem with diffusion models
Borong Zhang, Mart“́n Guerra, Qin Li, and Leonardo Zepeda-Núñez · 2024
Closest in time.