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 …
Modern Koopman theory for dynamical systems
The field of dynamical systems is being transformed by the mathematical tools and
algorithms emerging from modern computing and data science. First-principles derivations …
algorithms emerging from modern computing and data science. First-principles derivations …
On neural differential equations
P Kidger - arXiv preprint arXiv:2202.02435, 2022 - arxiv.org
The conjoining of dynamical systems and deep learning has become a topic of great
interest. In particular, neural differential equations (NDEs) demonstrate that neural networks …
interest. In particular, neural differential equations (NDEs) demonstrate that neural networks …
Machine learning–accelerated computational fluid dynamics
Numerical simulation of fluids plays an essential role in modeling many physical
phenomena, such as weather, climate, aerodynamics, and plasma physics. Fluids are well …
phenomena, such as weather, climate, aerodynamics, and plasma physics. Fluids are well …
Digital twin: Values, challenges and enablers from a modeling perspective
Digital twin can be defined as a virtual representation of a physical asset enabled through
data and simulators for real-time prediction, optimization, monitoring, controlling, and …
data and simulators for real-time prediction, optimization, monitoring, controlling, and …
Modal analysis of fluid flows: Applications and outlook
THE field of fluid mechanics involves a range of rich and vibrant problems with complex
dynamics stemming from instabilities, nonlinearities, and turbulence. The analysis of these …
dynamics stemming from instabilities, nonlinearities, and turbulence. The analysis of these …
基于人工神经网络的湍流大涡模拟方法
谢晨月， 袁泽龙， 王建春， 万敏平， 陈十一 - 力学学报, 2021 - lxxb.cstam.org.cn
大涡模拟方法(LES) 是研究复杂湍流问题的重要工具, 在航空航天, 湍流燃烧, 气动声学,
大气边界层等众多工程领域中具有广泛的应用前景. 大涡模拟方法采用粗网格计算大尺度上的 …
大气边界层等众多工程领域中具有广泛的应用前景. 大涡模拟方法采用粗网格计算大尺度上的 …
Unsupervised deep learning for super-resolution reconstruction of turbulence
Recent attempts to use deep learning for super-resolution reconstruction of turbulent flows
have used supervised learning, which requires paired data for training. This limitation …
have used supervised learning, which requires paired data for training. This limitation …
A point-cloud deep learning framework for prediction of fluid flow fields on irregular geometries
We present a novel deep learning framework for flow field predictions in irregular domains
when the solution is a function of the geometry of either the domain or objects inside the …
when the solution is a function of the geometry of either the domain or objects inside the …
A concise guide to modelling the physics of embodied intelligence in soft robotics
Embodied intelligence (intelligence that requires and leverages a physical body) is a well-
known paradigm in soft robotics, but its mathematical description and consequent …
known paradigm in soft robotics, but its mathematical description and consequent …