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 …

Discovering causal relations and equations from data

G Camps-Valls, A Gerhardus, U Ninad, G Varando… - Physics Reports, 2023 - Elsevier
Physics is a field of science that has traditionally used the scientific method to answer
questions about why natural phenomena occur and to make testable models that explain the …

Super-resolution analysis via machine learning: a survey for fluid flows

K Fukami, K Fukagata, K Taira - Theoretical and Computational Fluid …, 2023 - Springer
This paper surveys machine-learning-based super-resolution reconstruction for vortical
flows. Super resolution aims to find the high-resolution flow fields from low-resolution data …

[HTML][HTML] Improving aircraft performance using machine learning: A review

S Le Clainche, E Ferrer, S Gibson, E Cross… - Aerospace Science and …, 2023 - Elsevier
This review covers the new developments in machine learning (ML) that are impacting the
multi-disciplinary area of aerospace engineering, including fundamental fluid dynamics …

[HTML][HTML] A review of physics-based machine learning in civil engineering

SR Vadyala, SN Betgeri, JC Matthews… - Results in Engineering, 2022 - Elsevier
The recent development of machine learning (ML) and Deep Learning (DL) increases the
opportunities in all the sectors. ML is a significant tool that can be applied across many …

β-Variational autoencoders and transformers for reduced-order modelling of fluid flows

A Solera-Rico, C Sanmiguel Vila… - Nature …, 2024 - nature.com
Variational autoencoder architectures have the potential to develop reduced-order models
for chaotic fluid flows. We propose a method for learning compact and near-orthogonal …

Deep reinforcement learning for turbulent drag reduction in channel flows

L Guastoni, J Rabault, P Schlatter, H Azizpour… - The European Physical …, 2023 - Springer
We introduce a reinforcement learning (RL) environment to design and benchmark control
strategies aimed at reducing drag in turbulent fluid flows enclosed in a channel. The …

[HTML][HTML] Three-dimensional ESRGAN for super-resolution reconstruction of turbulent flows with tricubic interpolation-based transfer learning

L Yu, MZ Yousif, M Zhang, S Hoyas, R Vinuesa… - Physics of …, 2022 - pubs.aip.org
Turbulence is a complicated phenomenon because of its chaotic behavior with multiple
spatiotemporal scales. Turbulence also has irregularity and diffusivity, making predicting …

A transformer-based synthetic-inflow generator for spatially develo** turbulent boundary layers

MZ Yousif, M Zhang, L Yu, R Vinuesa… - Journal of Fluid …, 2023 - cambridge.org
This study proposes a newly developed deep-learning-based method to generate turbulent
inflow conditions for spatially develo** turbulent boundary layer (TBL) simulations. A …

[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 …