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Enhancing computational fluid dynamics with machine learning
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 …
Discovering causal relations and equations from data
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 …
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
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 …
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
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 …
[HTML][HTML] A review of physics-based machine learning in civil engineering
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 …
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
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 …
for chaotic fluid flows. We propose a method for learning compact and near-orthogonal …
Deep reinforcement learning for turbulent drag reduction in channel flows
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 …
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
Turbulence is a complicated phenomenon because of its chaotic behavior with multiple
spatiotemporal scales. Turbulence also has irregularity and diffusivity, making predicting …
spatiotemporal scales. Turbulence also has irregularity and diffusivity, making predicting …
A transformer-based synthetic-inflow generator for spatially develo** turbulent boundary layers
This study proposes a newly developed deep-learning-based method to generate turbulent
inflow conditions for spatially develo** turbulent boundary layer (TBL) simulations. A …
inflow conditions for spatially develo** turbulent boundary layer (TBL) simulations. A …
[PDF][PDF] The potential of machine learning to enhance computational fluid dynamics
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 …