Fluid Mechanics ML Papers

• Backpropagation Algorithms and Reservoir Computing in Recurrent Neural Networks for the Forecasting of Complex Spatiotemporal Dynamics
• Unsupervised deep learning for super-resolution reconstruction of turbulence
• From coarse wall measurements to turbulent velocity fields through deep learning
• Nonlinear mode decomposition with convolutional neural networks for fluid dynamics
• Stable a posteriori LES of 2D turbulence using convolutional neural networks: Backscattering analysis and generalization to higher Re via transfer learning
• An interpretable framework of data-driven turbulence modeling using deep neural networks
• A perspective on machine learning in turbulent flows
• Echo State Network for two-dimensional turbulent moist Rayleigh-Benard convection
• A machine learning framework for LES closure terms
• A Tutorial on the Proper Orthogonal Decomposition
• A deep learning enabler for non-intrusive reduced order modeling of fluid flows
• Turbulence Modeling in the Age of Data
• Comparison of direct numerical simulation databases of turbulent channel flow at Reτ = 180
• Convolutional Neural Network Models and Interpretability for the Anisotropic Reynolds Stress Tensor in Turbulent One-dimensional Flows
• Sub-grid modelling for two-dimensional turbulence using neural networks
• Physics-Informed Neural Networks: A Deep Learning Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations
• A Perspective on Machine Learning Methods in Turbulence Modelling
• Physics guided machine learning using simplified theories
• Machine learning accelerated computational fluid dynamics
• Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders
• A Neural Network based Shock Detection and Localization Approach for Discontinuous Galerkin Methods
• Neural Network Models for the Anisotropic Reynolds Stress Tensor in Turbulent Channel Flow
• Data-assisted reduced-order modeling of extreme events in complex dynamical systems
• Statistics of spatial derivatives of velocity and pressure in turbulent channel flow
• Convolutional neural network and long short-term memory based reduced order surrogate for minimal turbulent channel flow
• DeepCFD: Efficient Steady-State Laminar Flow Approximation with Deep Convolutional Neural Networks
• Machine Learning for Fluid Mechanics
• Physics-Informed Spatiotemporal Deep Learning for Emulating Coupled Dynamical Systems
• Time-series learning of latent-space dynamics for reduced-order model closure
• Data-Driven Fractional Subgrid-scale Modeling for Scalar Turbulence: A Nonlocal LES Approach
• Deep learning in turbulent convection networks
• Reservoir computing model of two-dimensional turbulent convection
• Predictions of turbulent shear ows using deep neural networks
• Transitional–turbulent spots and turbulent–turbulent spots in boundary layers
• Data-driven algebraic models of the turbulent Prandtl number for buoyancy-affected flow near a vertical surface
• From Deep to Physics-Informed Learning of Turbulence: Diagnostics
• Reynolds Averaged Turbulence Modeling using Deep Neural Networks with Embedded Invariance
• Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
• Application of machine learning algorithms to flow modeling and optimization
• History effects and near-equilibrium in adverse-pressure-gradient turbulent boundary layers
• Engine Combustion System Optimization Using Computational Fluid Dynamics and Machine Learning: A Methodological Approach