Machine learning the deuteron

Pfau D, Spencer J S, Matthews A G d G and Foulkes W M C 2019 Ab-Initio Solution of the Many-Electron Schrödinger Equation with Deep Neural Networks arxiv:1909.02487 URL http://arxiv.org/abs/1909.02487

Original

1909

Earlier work this paper cites.

Hermann J, Schätzle Z and Noé F 2019 Deep neural network solution of the electronic Schrödinger equation arxiv:1909.08423 URL http://arxiv.org/abs/1909.08423

Original

1909

Earlier work this paper cites.

Freitas P S A and Clark J W 2019 Experiments in machine learning of alpha-decay half-lives ( Preprint

Original

1910

Earlier work this paper cites.

Cybenko G 1989 Math. Control. Signals, Syst

1989

Earlier work this paper cites.

Hornik K 1991 Neural Networks

1991

Earlier work this paper cites.

Gazula S, Clark J and Bohr H 1992 Nucl. Phys. A

1992

Earlier work this paper cites.

Gernoth K, Clark J, Prater J and Bohr H 1993 Phys. Lett. B

1993

Earlier work this paper cites.

Hochreiter S, Bengio Y, Frasconi P and Schmidhuber J 2001 A Field Guide to Dynamical Recurrent Networks

2001

Earlier work this paper cites.

MacKay D J C 2003 Information Theory, Inference, and Learning Algorithms

2003

Earlier work this paper cites.

Entem D R and Machleidt R 2003 Phys. Rev. C

2003

Earlier work this paper cites.

Feindt M and Kerzel U 2006 Nucl. Instrum. Meth. A

2006

Earlier work this paper cites.

Bogner S and Furnstahl R 2006 Phys. Lett. B

2006

Earlier work this paper cites.

Bogner S and Furnstahl R 2006 Phys. Lett. B

2006

Earlier work this paper cites.

Adams C, Carleo G, Lovato A and Rocco N 2020 Variational Monte Carlo calculations of A ≤ 4 A\leq 4 nuclei with an artificial neural-network correlator ansatz arxiv:2007.14282 URL http://arxiv.org/abs/2007.14282

Original

2007

Cited alongside, same era.

Ball R D, Debbio L D, Forte S, Guffanti A, Latorre J I, Rojo J and Ubiali M 2010 Nuc. Phys. B

2010

Cited alongside, same era.

Anderson E R, Bogner S K, Furnstahl R J and Perry R J 2010 Phys. Rev. C

2010

Cited alongside, same era.

Glorot X and Bengio Y 2010 Understanding the difficulty of training deep feedforward neural networks Proceedings of the thirteenth international conference on artificial intelligence and statistics

2010

Cited alongside, same era.

Tieleman T and Hinton G 2012 Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude COURSERA: Neural networks for machine learning URL https://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf

Choo K, Carleo G, Regnault N and Neupert T 2018 Phys. Rev. Lett

2018

Later among the works it cites.

Saito H 2018 J. Phys. Soc. Japan

2018

Later among the works it cites.

Dumitrescu E F, McCaskey A J, Hagen G, Jansen G R, Morris T D, Papenbrock T, Pooser R C, Dean D J and Lougovski P 2018 Phys. Rev. Lett

2018

Later among the works it cites.

Wang Z A, Pei J, Liu Y and Qiang Y 2019 Phys. Rev. Lett

2019

Closest in time.

Niu Z M, Liang H Z, Sun B H, Long W H and Niu Y F 2019 Phys. Rev. C

2019

Closest in time.

Jiang W G, Hagen G and Papenbrock T 2019 Phys. Rev. C

2019

Closest in time.