Fetching the paper…
Reading the bibliography…
In this note we propose a method based on artificial neural network to study the transition between states governed by stochastic processes.
Artificial neural networks for solving ordinary and partial differential equations
I. E. Lagaris, A. Likas, and D. I. Fotiadis · 1998
Earlier work this paper cites.
Finite temparture string method for the study of rare events
W. E, W. Ren, and E. Vanden-Eijnden · 2005
Earlier work this paper cites.
Diffusion maps
R. R. Coifman and S. Lafon · 2006
Earlier work this paper cites.
Towards a theory of transition paths
W. E and E. Vanden-Eijnden · 2006
Earlier work this paper cites.
Reducing the dimensionality of data with neural networks
G. E. Hinton and R. R. Salakhutdinov · 2006
Earlier work this paper cites.
Diffusion maps, reduction coordinates, and low dimensional representation of stochastic systems
R. R. Coifman, I. G. Kevrekidis, S. Lafon, M. Maggioni, and B. Nadler · 2008
Earlier work this paper cites.
Revisiting the finite temperature string method for the calculation of reaction tubes and free energies
E. Vanden-Eijnden and M. Venturoli · 2009
Cited alongside, same era.
Transition path theory and path-finding algorithms for the study of rare events
W. E and E. Vanden-Eijnden · 2010
Cited alongside, same era.
Adam: A method for stochastic optimization
D. Kingma and J. Ba · 2014
Cited alongside, same era.
Deep learning
Y. LeCun, Y. Bengio, and G. Hinton · 2015
Cited alongside, same era.
Reactive trajectories and the transition path process
J. Lu and J. Nolen · 2015
Cited alongside, same era.
Deep learning in neural networks: An overview
J. Schmidhuber · 2015
Cited alongside, same era.
Tensorflow: Large-scale machine learning on heterogeneous distributed systems
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, J. Kudlur, Manjunath Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng · 2016
Later among the works it cites.
J. Berg and K. Nyström · 2017
Later among the works it cites.
Solving the quantum many-body problem with artificial neural networks
G. Carleo and M. Troyer · 2017
Later among the works it cites.
Point cloud discretization of Fokker-Planck operators for committor functions
R. Lai and J. Lu · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
The deep Ritz method: A deep learning-based numerical algorithm for solving variational problems
W. E and B. Yu
Cited in the paper.
J. Sirignano and K. Spiliopoulos · 2017
Later among the works it cites.
Reinforced dynamics of large atomic and molecular systems
L. Zhang, H. Wang, and W. E · 2017
Later among the works it cites.