TSI-SD: A time-sequence-involved space discretization neural network for passive scalar advection in a two-dimensional unsteady flow

Physics-informed neural networks (PINNs) for fluid mechanics: a review

(2022) Shengze Cai et al.   ACTA MECHANICA SINICA

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

Machine learning–accelerated computational fluid dynamics

(2021) Dmitrii Kochkov et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics

(2021) Weiqi Ji et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Learned discretizations for passive scalar advection in a two-dimensional turbulent flow

(2021) Jiawei Zhuang et al.   Physical Review Fluids

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

Rigorous numerical computations for 1D advection equations with variable coefficients

(2019) Akitoshi Takayasu et al.   JAPAN JOURNAL OF INDUSTRIAL AND APPLIED MATHEMATICS

Learning data-driven discretizations for partial differential equations

(2019) Yohai Bar-Sinai et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

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

Comment on “Diffusion by a random velocity field” [Phys. Fluids 13, 22 (1970)]

(2016) Tony Saad et al.   PHYSICS OF FLUIDS

Inflow-implicit/outflow-explicit finite volume methods for solving advection equations

(2014) Karol Mikula et al.   APPLIED NUMERICAL MATHEMATICS

Accelerating fast fluid dynamics with a coarse-grid projection scheme

(2014) Mingang Jin et al.   HVAC&R RESEARCH