A generalized framework for unsupervised learning and data recovery in computational fluid dynamics using discretized loss functions

A twin-decoder structure for incompressible laminar flow reconstruction with uncertainty estimation around 2D obstacles

(2022) J. Chen et al.   NEURAL COMPUTING & APPLICATIONS

Flexible cylinder flow-induced vibration

(2022) Leixin Ma et al.   PHYSICS OF FLUIDS

Analysis on numerical stability and convergence of Reynolds averaged Navier–Stokes simulations from the perspective of coupling modes

(2022) Yilang Liu et al.   PHYSICS OF FLUIDS

3D Ship Hull Encoding and Optimization via Deep Neural Networks

(2022) Yuyang Wang et al.   JOURNAL OF MECHANICAL DESIGN

Minimal error partially resolving simulation methods for turbulent flows: A dynamic machine learning approach

(2022) Stefan Heinz   PHYSICS OF FLUIDS

Three-dimensional deep learning-based reduced order model for unsteady flow dynamics with variable Reynolds number

(2022) Rachit Gupta et al.   PHYSICS OF FLUIDS

DiscretizationNet: A machine-learning based solver for Navier–Stokes equations using finite volume discretization

(2021) Rishikesh Ranade et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

A convolutional strategy on unstructured mesh for the adjoint vector modeling

(2021) Mengfei Xu et al.   PHYSICS OF FLUIDS

Single-step deep reinforcement learning for open-loop control of laminar and turbulent flows

(2021) H. Ghraieb et al.   Physical Review Fluids

A review on deep reinforcement learning for fluid mechanics

(2021) Paul Garnier et al.   COMPUTERS & FLUIDS

Machine learning-aided design optimization of a mechanical micromixer

(2021) F.-J. Granados-Ortiz et al.   PHYSICS OF FLUIDS

A hybrid partitioned deep learning methodology for moving interface and fluid–structure interaction

(2021) Rachit Gupta et al.   COMPUTERS & FLUIDS

Graph neural networks for laminar flow prediction around random two-dimensional shapes

(2021) J. Chen et al.   PHYSICS OF FLUIDS

Robust deep learning for emulating turbulent viscosities

(2021) Aakash Patil et al.   PHYSICS OF FLUIDS

Deep learning for reduced order modelling and efficient temporal evolution of fluid simulations

(2021) Pranshu Pant et al.   PHYSICS OF FLUIDS

High Reynolds number airfoil turbulence modeling method based on machine learning technique

(2021) Xuxiang Sun et al.   COMPUTERS & FLUIDS

Machine learning for nonintrusive model order reduction of the parametric inviscid transonic flow past an airfoil

(2020) S. Ashwin Renganathan et al.   PHYSICS OF FLUIDS

Data-driven recovery of hidden physics in reduced order modeling of fluid flows

(2020) Suraj Pawar et al.   PHYSICS OF FLUIDS

Conservative physics-informed neural networks on discrete domains for conservation laws: Applications to forward and inverse problems

(2020) Ameya D. Jagtap et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Deep-learning-based surrogate model for reservoir simulation with time-varying well controls

(2020) Zhaoyang Larry Jin et al.   JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING

Efficient uncertainty quantification for dynamic subsurface flow with surrogate by Theory-guided Neural Network

(2020) Nanzhe Wang et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

hp-VPINNs: Variational physics-informed neural networks with domain decomposition

(2020) Ehsan Kharazmi et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Direct shape optimization through deep reinforcement learning

(2020) Jonathan Viquerat et al.   JOURNAL OF COMPUTATIONAL PHYSICS

NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations

(2020) Xiaowei Jin et al.   JOURNAL OF COMPUTATIONAL PHYSICS

A turbulent eddy-viscosity surrogate modeling framework for Reynolds-averaged Navier-Stokes simulations

(2020) Romit Maulik et al.   COMPUTERS & FLUIDS

Machine learning methods for turbulence modeling in subsonic flows around airfoils

(2019) Linyang Zhu et al.   PHYSICS OF FLUIDS

Epistemic uncertainty quantification for Reynolds-averaged Navier-Stokes modeling of separated flows over streamlined surfaces

(2019) C. Gorlé et al.   PHYSICS OF FLUIDS

Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data

(2019) Yinhao Zhu et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Fast flow field prediction over airfoils using deep learning approach

(2019) Vinothkumar Sekar et al.   PHYSICS OF FLUIDS

Accelerating deep reinforcement learning strategies of flow control through a multi-environment approach

(2019) Jean Rabault et al.   PHYSICS OF FLUIDS

Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data

(2019) Luning Sun et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Towards a general data-driven explicit algebraic Reynolds stress prediction framework

(2019) Corrado Sotgiu et al.   INTERNATIONAL JOURNAL OF HEAT AND FLUID FLOW

Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks

(2019) Nicholas Geneva et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Physics Informed Extreme Learning Machine (PIELM)–A rapid method for the numerical solution of partial differential equations

(2019) Vikas Dwivedi et al.   NEUROCOMPUTING

Bayesian deep convolutional encoder–decoder networks for surrogate modeling and uncertainty quantification

(2018) Yinhao Zhu et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework

(2018) Jin-Long Wu et al.   Physical Review Fluids

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

(2018) M. Raissi et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data

(2017) Jian-Xun Wang et al.   Physical Review Fluids

Reynolds averaged turbulence modelling using deep neural networks with embedded invariance

(2016) Julia Ling et al.   JOURNAL OF FLUID MECHANICS