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

StressGAN: A Generative Deep Learning Model for 2D Stress Distribution Prediction

(2021) Haoliang Jiang et al.   JOURNAL OF APPLIED MECHANICS-TRANSACTIONS OF THE ASME

Zooming method for FEA using a neural network

(2021) Taichi Yamaguchi et al.   COMPUTERS & STRUCTURES

A physics-guided neural network framework for elastic plates: Comparison of governing equations-based and energy-based approaches

(2021) Wei Li et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Recurrent neural network-based multiaxial plasticity model with regularization for physics-informed constraints

(2021) L. Borkowski et al.   COMPUTERS & STRUCTURES

Extreme theory of functional connections: A fast physics-informed neural network method for solving ordinary and partial differential equations

(2021) Enrico Schiassi et al.   NEUROCOMPUTING

Transfer learning based multi-fidelity physics informed deep neural network

(2020) Souvik Chakraborty   JOURNAL OF COMPUTATIONAL PHYSICS

Selection of element-wise shell kinematics using neural networks

(2020) M. Petrolo et al.   COMPUTERS & STRUCTURES

Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems

(2019) Dongkun Zhang et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Adaptive activation functions accelerate convergence in deep and physics-informed neural networks

(2019) Ameya D. Jagtap et al.   JOURNAL OF COMPUTATIONAL PHYSICS

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

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

Deep learning

(2015) Yann LeCun et al.   NATURE

A review on the mechanical behaviour of curvilinear fibre composite laminated panels

(2013) Pedro Ribeiro et al.   JOURNAL OF COMPOSITE MATERIALS

Buckling analysis and optimisation of variable angle tow composite plates

(2012) Zhangming Wu et al.   THIN-WALLED STRUCTURES