Predicting high-fidelity multiphysics data from low-fidelity fluid flow and transport solvers using physics-informed neural networks

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

(2022) Hamidreza Eivazi et al.   PHYSICS OF FLUIDS

Physics guided machine learning using simplified theories

(2021) Suraj Pawar et al.   PHYSICS OF FLUIDS

Efficient training of physics‐informed neural networks via importance sampling

(2021) Mohammad Amin Nabian et al.   COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING

Physics-Informed Neural Networks for Heat Transfer Problems

(2021) Shengze Cai et al.   JOURNAL OF HEAT TRANSFER-TRANSACTIONS OF THE ASME

Centrifugal buoyancy in a rotating fluid layer next to and distant from the rotation axis

(2021) Peter Vadasz   PHYSICS OF FLUIDS

Uncovering near-wall blood flow from sparse data with physics-informed neural networks

(2021) Amirhossein Arzani et al.   PHYSICS OF FLUIDS

PPINN: Parareal physics-informed neural network for time-dependent PDEs

(2020) Xuhui Meng et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Transfer learning based multi-fidelity physics informed deep neural network

(2020) Souvik Chakraborty   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

Multi-Fidelity Physics-Constrained Neural Network and Its Application in Materials Modeling

(2019) Dehao Liu et al.   JOURNAL OF MECHANICAL DESIGN

A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems

(2019) Xuhui Meng et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Transfer learning enhanced physics informed neural network for phase-field modeling of fracture

(2019) Somdatta Goswami et al.   THEORETICAL AND APPLIED FRACTURE MECHANICS

Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization

(2018) Benjamin Peherstorfer et al.   SIAM REVIEW

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

Narrowing the Expertise Gap for Predicting Intracranial Aneurysm Hemodynamics: Impact of Solver Numerics versus Mesh and Time-Step Resolution

(2015) M. O. Khan et al.   AMERICAN JOURNAL OF NEURORADIOLOGY

Characterization of the transport topology in patient-specific abdominal aortic aneurysm models

(2012) Amirhossein Arzani et al.   PHYSICS OF FLUIDS