Using physics-informed neural networks with small datasets to predict the length of gas turbine nozzle cracks

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

(2022) Maryam Aliakbari et al.   INTERNATIONAL JOURNAL OF HEAT AND FLUID FLOW

Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next

(2022) Salvatore Cuomo et al.   JOURNAL OF SCIENTIFIC COMPUTING

Power prediction of wind turbine in the wake using hybrid physical process and machine learning models

(2022) Huanyu Zhou et al.   RENEWABLE ENERGY

Hybrid physics-informed neural networks for main bearing fatigue prognosis with visual grease inspection

(2021) Yigit A. Yucesan et al.   COMPUTERS IN INDUSTRY

A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics

(2021) Ehsan Haghighat et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Predicting length of fatigue cracks by means of machine learning algorithms in the small-data regime

(2021) Maciej Badora et al.   Eksploatacja i Niezawodnosc-Maintenance and Reliability

Mosaic flows: A transferable deep learning framework for solving PDEs on unseen domains

(2021) Hengjie Wang et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

A novel approach to predict shear strength of tilted angle connectors using artificial intelligence techniques

(2020) Mahdi Shariati et al.   ENGINEERING WITH COMPUTERS

Physics-informed neural networks for missing physics estimation in cumulative damage models: a case study in corrosion fatigue

(2020) Arinan Dourado et al.   JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING

Industrial gas turbine operating parameters monitoring and data-driven prediction

(2020) Maciej Pawełczyk et al.   Eksploatacja i Niezawodnosc-Maintenance and Reliability

Array programming with NumPy

(2020) Charles R. Harris et al.   NATURE

Distributed learning machines for solving forward and inverse problems in partial differential equations

(2020) Vikas Dwivedi et al.   NEUROCOMPUTING

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

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

Reliability analysis of gas turbine engine by means of bathtub‐shaped failure rate distribution

(2019) Shazaib Ahsan et al.   PROCESS SAFETY PROGRESS

Gas turbine performance prediction via machine learning

(2019) Zuming Liu et al.   ENERGY

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

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