[HTML][HTML] Deep learning to replace, improve, or aid CFD analysis in built environment applications: A review
G Calzolari, W Liu - Building and Environment, 2021 - Elsevier
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 …
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 …
Physics-informed machine learning: A survey on problems, methods and applications
Recent advances of data-driven machine learning have revolutionized fields like computer
vision, reinforcement learning, and many scientific and engineering domains. In many real …
vision, reinforcement learning, and many scientific and engineering domains. In many real …
Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems
Despite the great promise of the physics-informed neural networks (PINNs) in solving
forward and inverse problems, several technical challenges are present as roadblocks for …
forward and inverse problems, several technical challenges are present as roadblocks for …
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 …
Dense velocity reconstruction from particle image velocimetry/particle tracking velocimetry using a physics-informed neural network
The velocities measured by particle image velocimetry (PIV) and particle tracking
velocimetry (PTV) commonly provide sparse information on flow motions. A dense velocity …
velocimetry (PTV) commonly provide sparse information on flow motions. A dense velocity …
Physics-informed deep learning for computational elastodynamics without labeled data
Numerical methods such as finite element have been flourishing in the past decades for
modeling solid mechanics problems via solving governing partial differential equations …
modeling solid mechanics problems via solving governing partial differential equations …
Physics-informed PointNet: A deep learning solver for steady-state incompressible flows and thermal fields on multiple sets of irregular geometries
We present a novel physics-informed deep learning framework for solving steady-state
incompressible flow on multiple sets of irregular geometries by incorporating two main …
incompressible flow on multiple sets of irregular geometries by incorporating two main …
Super-resolution generative adversarial networks of randomly-seeded fields
Reconstruction of field quantities from sparse measurements is a problem arising in a broad
spectrum of applications. This task is particularly challenging when the mapping between …
spectrum of applications. This task is particularly challenging when the mapping between …
Physics-informed neural networks for phase-field method in two-phase flow
R Qiu, R Huang, Y Xiao, J Wang, Z Zhang, J Yue… - Physics of …, 2022 - pubs.aip.org
The complex flow modeling based on machine learning is becoming a promising way to
describe multiphase fluid systems. This work demonstrates how a physics-informed neural …
describe multiphase fluid systems. This work demonstrates how a physics-informed neural …