Promising directions of machine learning for partial differential equations
Partial differential equations (PDEs) are among the most universal and parsimonious
descriptions of natural physical laws, capturing a rich variety of phenomenology and …
descriptions of natural physical laws, capturing a rich variety of phenomenology and …
[HTML][HTML] Deep learning to replace, improve, or aid CFD analysis in built environment applications: A review
Fast and accurate airflow simulations in the built environment are critical to provide
acceptable thermal comfort and air quality to the occupants. Computational Fluid Dynamics …
acceptable thermal comfort and air quality to the occupants. Computational Fluid Dynamics …
Review of machine learning for hydrodynamics, transport, and reactions in multiphase flows and reactors
Artificial intelligence (AI), machine learning (ML), and data science are leading to a
promising transformative paradigm. ML, especially deep learning and physics-informed ML …
promising transformative paradigm. ML, especially deep learning and physics-informed ML …
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 …
Uncovering near-wall blood flow from sparse data with physics-informed neural networks
Near-wall blood flow and wall shear stress (WSS) regulate major forms of cardiovascular
disease, yet they are challenging to quantify with high fidelity. Patient-specific computational …
disease, yet they are challenging to quantify with high fidelity. Patient-specific computational …
A physics-informed diffusion model for high-fidelity flow field reconstruction
Abstract Machine learning models are gaining increasing popularity in the domain of fluid
dynamics for their potential to accelerate the production of high-fidelity computational fluid …
dynamics for their potential to accelerate the production of high-fidelity computational fluid …
Perspectives on machine learning-augmented Reynolds-averaged and large eddy simulation models of turbulence
K Duraisamy - Physical Review Fluids, 2021 - APS
This work presents a review and perspectives on recent developments in the use of machine
learning (ML) to augment Reynolds-averaged Navier-Stokes (RANS) and large eddy …
learning (ML) to augment Reynolds-averaged Navier-Stokes (RANS) and large eddy …
[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 …
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 …