Transfer physics informed neural network: a new framework for distributed physics informed neural networks via parameter sharing

Solving inverse heat transfer problems without surrogate models - a fast, data-sparse, Physics Informed Neural Network approach

(2022) Vivek Oommen et al.   JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING

Physics informed neural networks for continuum micromechanics

(2022) Alexander Henkes et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

A framework based on physics-informed neural networks and extreme learning for the analysis of composite structures

(2022) C.A. Yan et al.   COMPUTERS & STRUCTURES

Machine learning for metal additive manufacturing: predicting temperature and melt pool fluid dynamics using physics-informed neural networks

(2021) Qiming Zhu et al.   COMPUTATIONAL MECHANICS

A physics-informed machine learning approach for solving heat transfer equation in advanced manufacturing and engineering applications

(2021) Navid Zobeiry et al.   ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

Neural network method: delay and system of delay differential equations

(2021) Shagun Panghal et al.   ENGINEERING WITH COMPUTERS

Physics-Informed Neural Networks for Heat Transfer Problems

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

Physics informed neural networks for simulating radiative transfer

(2021) Siddhartha Mishra et al.   JOURNAL OF QUANTITATIVE SPECTROSCOPY & RADIATIVE TRANSFER

A physics-informed deep learning method for solving direct and inverse heat conduction problems of materials

(2021) Zhili He et al.   Materials Today Communications

Physics-informed neural network for modelling the thermochemical curing process of composite-tool systems during manufacture

(2021) Sina Amini Niaki et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Parametric deep energy approach for elasticity accounting for strain gradient effects

(2021) Vien Minh Nguyen-Thanh et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Local extreme learning machines and domain decomposition for solving linear and nonlinear partial differential equations

(2021) Suchuan Dong et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

SPINN: Sparse, Physics-based, and partially Interpretable Neural Networks for PDEs

(2021) Amuthan A. Ramabathiran et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Optimization free neural network approach for solving ordinary and partial differential equations

(2020) Shagun Panghal et al.   ENGINEERING WITH COMPUTERS

An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications

(2020) E. Samaniego et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

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

Locally adaptive activation functions with slope recovery for deep and physics-informed neural networks

(2020) Ameya D. Jagtap et al.   PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES

SciANN: A Keras/TensorFlow wrapper for scientific computations and physics-informed deep learning using artificial neural networks

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

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

(2020) Vikas Dwivedi et al.   NEUROCOMPUTING

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

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

PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain

(2020) Han Gao 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

Bilinear neural network method to obtain the exact analytical solutions of nonlinear partial differential equations and its application to p-gBKP equation

(2019) Run-Fa Zhang et al.   NONLINEAR DYNAMICS

Prediction of ultimate bearing capacity through various novel evolutionary and neural network models

(2019) Hossein Moayedi et al.   ENGINEERING WITH COMPUTERS

A deep learning solution approach for high-dimensional random differential equations

(2019) Mohammad Amin Nabian et al.   PROBABILISTIC ENGINEERING MECHANICS

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

(2019) Vikas Dwivedi et al.   NEUROCOMPUTING

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

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

Prediction of building damage induced by tunnelling through an optimized artificial neural network

(2018) S. Moosazadeh et al.   ENGINEERING WITH COMPUTERS

A combination of artificial bee colony and neural network for approximating the safety factor of retaining walls

(2018) Ebrahim Noroozi Ghaleini et al.   ENGINEERING WITH COMPUTERS

Estimating and optimizing safety factors of retaining wall through neural network and bee colony techniques

(2018) Behrouz Gordan et al.   ENGINEERING WITH COMPUTERS

DGM: A deep learning algorithm for solving partial differential equations

(2018) Justin Sirignano et al.   JOURNAL OF COMPUTATIONAL PHYSICS

A unified deep artificial neural network approach to partial differential equations in complex geometries

(2018) Jens Berg et al.   NEUROCOMPUTING

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

Approximate solutions by artificial neural network of hybrid fuzzy differential equations

(2017) Mahmoud Paripour et al.   Advances in Mechanical Engineering

Artificial neural network approach for a class of fractional ordinary differential equation

(2016) Ahmad Jafarian et al.   NEURAL COMPUTING & APPLICATIONS

A constrained integration (CINT) approach to solving partial differential equations using artificial neural networks

(2015) Keith Rudd et al.   NEUROCOMPUTING

Heterogeneous Heat Conduction Problems by an Improved Element-Free Galerkin Method

(2014) Xiaohua Zhang et al.   NUMERICAL HEAT TRANSFER PART B-FUNDAMENTALS

Artificial neural networks in medical diagnosis

(2013) Filippo Amato et al.   Journal of Applied Biomedicine

The lid-driven square cavity flow: numerical solution with a 1024 x 1024 grid

(2010) Carlos Henrique Marchi et al.   Journal of the Brazilian Society of Mechanical Sciences and Engineering