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 …
A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics
We present the application of a class of deep learning, known as Physics Informed Neural
Networks (PINN), to inversion and surrogate modeling in solid mechanics. We explain how …
Networks (PINN), to inversion and surrogate modeling in solid mechanics. We explain how …
SciANN: A Keras/TensorFlow wrapper for scientific computations and physics-informed deep learning using artificial neural networks
E Haghighat, R Juanes - Computer Methods in Applied Mechanics and …, 2021 - Elsevier
In this paper, we introduce SciANN, a Python package for scientific computing and physics-
informed deep learning using artificial neural networks. SciANN uses the widely used deep …
informed deep learning using artificial neural networks. SciANN uses the widely used deep …
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 …
[HTML][HTML] Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels
High-resolution (HR) information of fluid flows, although preferable, is usually less
accessible due to limited computational or experimental resources. In many cases, fluid data …
accessible due to limited computational or experimental resources. In many cases, fluid data …
The transformative potential of machine learning for experiments in fluid mechanics
The field of machine learning (ML) has rapidly advanced the state of the art in many fields of
science and engineering, including experimental fluid dynamics, which is one of the original …
science and engineering, including experimental fluid dynamics, which is one of the original …
Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows
We present a new data reconstruction method with supervised machine learning techniques
inspired by super resolution and inbetweening to recover high-resolution turbulent flows …
inspired by super resolution and inbetweening to recover high-resolution turbulent flows …
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 …
Convolutional neural network and long short-term memory based reduced order surrogate for minimal turbulent channel flow
We investigate the applicability of the machine learning based reduced order model (ML-
ROM) to three-dimensional complex flows. As an example, we consider a turbulent channel …
ROM) to three-dimensional complex flows. As an example, we consider a turbulent channel …