Enhancing computational fluid dynamics with machine learning
R Vinuesa, SL Brunton - Nature Computational Science, 2022 - nature.com
Abstract Machine learning is rapidly becoming a core technology for scientific computing,
with numerous opportunities to advance the field of computational fluid dynamics. Here we …
with numerous opportunities to advance the field of computational fluid dynamics. Here we …
[HTML][HTML] Improving aircraft performance using machine learning: A review
This review covers the new developments in machine learning (ML) that are impacting the
multi-disciplinary area of aerospace engineering, including fundamental fluid dynamics …
multi-disciplinary area of aerospace engineering, including fundamental fluid dynamics …
Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equations
N McGreivy, A Hakim - Nature Machine Intelligence, 2024 - nature.com
One of the most promising applications of machine learning in computational physics is to
accelerate the solution of partial differential equations (PDEs). The key objective of machine …
accelerate the solution of partial differential equations (PDEs). The key objective of machine …
[PDF][PDF] The potential of machine learning to enhance computational fluid dynamics
R Vinuesa, SL Brunton - arXiv preprint arXiv:2110.02085, 2021 - researchgate.net
Abstract Machine learning is rapidly becoming a core technology for scientific computing,
with numerous opportunities to advance the field of computational fluid dynamics. This …
with numerous opportunities to advance the field of computational fluid dynamics. This …
Poisson CNN: Convolutional neural networks for the solution of the Poisson equation on a Cartesian mesh
The Poisson equation is commonly encountered in engineering, for instance, in
computational fluid dynamics (CFD) where it is needed to compute corrections to the …
computational fluid dynamics (CFD) where it is needed to compute corrections to the …
Performance and accuracy assessments of an incompressible fluid solver coupled with a deep convolutional neural network
The resolution of the Poisson equation is usually one of the most computationally intensive
steps for incompressible fluid solvers. Lately, DeepLearning, and especially convolutional …
steps for incompressible fluid solvers. Lately, DeepLearning, and especially convolutional …
Predicting the propagation of acoustic waves using deep convolutional neural networks
A novel approach for numerically propagating acoustic waves in two-dimensional quiescent
media has been developed through a fully convolutional multi-scale neural network …
media has been developed through a fully convolutional multi-scale neural network …
Using neural networks to solve the 2D Poisson equation for electric field computation in plasma fluid simulations
L Cheng, EA Illarramendi, G Bogopolsky… - arXiv preprint arXiv …, 2021 - arxiv.org
The Poisson equation is critical to get a self-consistent solution in plasma fluid simulations
used for Hall effect thrusters and streamer discharges, since the Poisson solution appears …
used for Hall effect thrusters and streamer discharges, since the Poisson solution appears …
Coaxial-injector surrogate modeling based on Reynolds-averaged Navier–Stokes simulations using deep learning
M Krügener, JF Zapata Usandivaras… - Journal of Propulsion …, 2022 - arc.aiaa.org
Facing the need to increase the accuracy of rocket engine design tools, the present work
introduces an innovative methodology for the design and optimization of rocket engine …
introduces an innovative methodology for the design and optimization of rocket engine …
A denoising diffusion model for fluid field prediction
G Yang, S Sommer - arXiv preprint arXiv:2301.11661, 2023 - arxiv.org
We propose a novel denoising diffusion generative model for predicting nonlinear fluid fields
named FluidDiff. By performing a diffusion process, the model is able to learn a complex …
named FluidDiff. By performing a diffusion process, the model is able to learn a complex …