Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data
We show that the discrepancies in Reynolds-averaged Navier-Stokes (RANS) modeled Reynolds stresses can be explained by mean flow features. A physics-informed machine learning framework is proposed to…
Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain
Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems
SSR-VFD: Spatial Super-Resolution for Vector Field Data Analysis and Visualization
SSR-VFD is the first work that advocates a machine learning approach to generate high-resolution vector fields from low-resolution ones, and lies in the use of three separate neural nets that take the three components of a low- Resolution vector field as input and jointly output a synthesized high- resolution vector field.
Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier-Stokes simulations: A data-driven, physics-informed Bayesian approach
Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels
This work presents a novel physics-informed DL-based SR solution using convolutional neural networks (CNN), which is able to produce HR flow fields from low-resolution (LR) inputs in high-dimensional parameter space by leveraging the conservation laws and boundary conditions of fluid flows.
A Priori Assessment of Prediction Confidence for Data-Driven Turbulence Modeling
The results show that the prediction error of the Reynolds stress anisotropy is positively correlated with Mahalanobis distance and KDE distance, demonstrating that both extrapolation metrics can be used to estimate the prediction confidence a priori.
A Comprehensive Physics-Informed Machine Learning Framework for Predictive Turbulence Modeling
Although an increased availability of computational resources has enabled high-fidelity simulations of turbulent flows, the RANS models are still the dominant tools for industrial applications.…
Prediction of Reynolds stresses in high-Mach-number turbulent boundary layers using physics-informed machine learning
- Jian-Xun Wang, Junji Huang, L. Duan, Heng Xiao
- Computer ScienceTheoretical and Computational Fluid Dynamics
- 19 August 2018
The PIML approach is demonstrated to be a computationally affordable technique for improving the accuracy of RANS-modeled Reynolds stresses for high-Mach-number turbulent flows when there is a lack of experiments and high-fidelity simulations.