Fourier neural operator with boundary conditions for efficient prediction of steady airfoil flows

An artificial viscosity augmented physics-informed neural network for incompressible flow

(2023) Yichuan He et al.   APPLIED MATHEMATICS AND MECHANICS-ENGLISH EDITION

Fourier Neural Operator Network for Fast Photoacoustic Wave Simulations

(2023) Steven Guan et al.   Algorithms

U-FNO—An enhanced Fourier neural operator-based deep-learning model for multiphase flow

(2022) Gege Wen et al.   ADVANCES IN WATER RESOURCES

Physics-informed neural networks for solving Reynolds-averaged Navier–Stokes equations

(2022) Hamidreza Eivazi et al.   PHYSICS OF FLUIDS

Fourier neural operator approach to large eddy simulation of three-dimensional turbulence

(2022) Zhijie Li et al.   Theoretical and Applied Mechanics Letters

Data-driven parametric soliton-rogon state transitions for nonlinear wave equations using deep learning with Fourier neural operator

(2022) Ming Zhong et al.   COMMUNICATIONS IN THEORETICAL PHYSICS

Unsteady aerodynamic reduced-order modeling based on machine learning across multiple airfoils

(2021) Kai Li et al.   AEROSPACE SCIENCE AND TECHNOLOGY

When and why PINNs fail to train: A neural tangent kernel perspective

(2021) Sifan Wang et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Fast pressure distribution prediction of airfoils using deep learning

(2020) Xinyu Hui et al.   AEROSPACE SCIENCE AND TECHNOLOGY

A physics-informed operator regression framework for extracting data-driven continuum models

(2020) Ravi G. Patel et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Non-invasive inference of thrombus material properties with physics-informed neural networks

(2020) Minglang Yin et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Prediction of aerodynamic flow fields using convolutional neural networks

(2019) Saakaar Bhatnagar et al.   COMPUTATIONAL MECHANICS

Deep Learning Methods for Reynolds-Averaged Navier–Stokes Simulations of Airfoil Flows

(2019) Nils Thuerey et al.   AIAA JOURNAL

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

Physics-informed neural networks for high-speed flows

(2019) Zhiping Mao et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

A deep learning approach for efficiently and accurately evaluating the flow field of supercritical airfoils

(2019) Haizhou Wu et al.   COMPUTERS & FLUIDS

Data-assisted reduced-order modeling of extreme events in complex dynamical systems

(2018) Zhong Yi Wan et al.   PLoS One

Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks

(2018) Pantelis R. Vlachas et al.   PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES

Deep learning of vortex-induced vibrations

(2018) Maziar Raissi et al.   JOURNAL OF FLUID MECHANICS

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

Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition

(2011) G. E. Dahl et al.   IEEE Transactions on Audio Speech and Language Processing