PSO-PINN: Physics-Informed Neural Networks Trained with Particle Swarm Optimization

K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators,” Neural networks

1989

Earlier work this paper cites.

L. K. Hansen and P. Salamon, “Neural network ensembles,” IEEE transactions on pattern analysis and machine intelligence

1990

Earlier work this paper cites.

D. H. Wolpert, “Stacked generalization,” Neural networks

1992

Earlier work this paper cites.

S.-i. Amari, “Backpropagation and stochastic gradient descent method,” Neurocomputing

1993

Earlier work this paper cites.

J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of ICNN’95-international conference on neural networks

1995

Earlier work this paper cites.

R. Eberhart and J. Kennedy, “Particle swarm optimization,” in Proceedings of the IEEE international conference on neural networks

1995

Earlier work this paper cites.

Y. Shi and R. C. Eberhart, “Empirical study of particle swarm optimization,” in Proceedings of the 1999 congress on evolutionary computation-CEC99 (Cat. No. 99TH8406)

1999

Earlier work this paper cites.

F. Van den Bergh and A. P. Engelbrecht, “Cooperative learning in neural networks using particle swarm optimizers,” South African Computer Journal

2000

Earlier work this paper cites.

Cambridge university press, 2002

R. J. LeVeque et al · 2002

Earlier work this paper cites.

X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks,” in Proceedings of the thirteenth international conference on artificial intelligence and statistics

2010

Earlier work this paper cites.

X. Glorot, A. Bordes, and Y. Bengio, “Deep sparse rectifier neural networks,” in Proceedings of the fourteenth international conference on artificial intelligence and statistics

2011

Earlier work this paper cites.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Advances in neural information processing systems

2012

Earlier work this paper cites.

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980

Original

2014

Earlier work this paper cites.

G. Das, P. K. Pattnaik, and S. K. Padhy, “Artificial neural network trained by particle swarm optimization for non-linear channel equalization,” Expert Systems with Applications

2014

Cited alongside, same era.

L. Deng and J. Platt, “Ensemble deep learning for speech recognition,” in Proc. interspeech

2014

Cited alongside, same era.

X. Qiu, L. Zhang, Y. Ren, P. N. Suganthan, and G. Amaratunga, “Ensemble deep learning for regression and time series forecasting,” in 2014 IEEE symposium on computational intelligence in ensemble learning (CIEL)

2014

Cited alongside, same era.

S. Mirjalili, “How effective is the grey wolf optimizer in training multi-layer perceptrons,” Applied Intelligence

2015

Cited alongside, same era.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” nature

2015

Cited alongside, same era.

M. Raissi, A. Yazdani, and G. E. Karniadakis, “Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations,” Science

2020

Later among the works it cites.

L. McClenny and U. Braga-Neto, “Self-adaptive physics-informed neural networks using a soft attention mechanism,” arXiv preprint arXiv:2009.04544

Original

2020

Later among the works it cites.

A. D. Jagtap and G. E. Karniadakis, “Extended physics-informed neural networks (xpinns): A generalized space-time domain decomposition based deep learning framework for nonlinear partial differential equations,” Communications in Computational Physics

2020

Later among the works it cites.

S. J. Mousavirad, G. Schaefer, S. M. J. Jalali, and I. Korovin, “A benchmark of recent population-based metaheuristic algorithms for multi-layer neural network training,” in Proceedings of the 2020 genetic and evolutionary computation conference companion

2020

Later among the works it cites.