Fetching the paper…
Reading the bibliography…
We assume that we are given a time series of data from a dynamical system and our task is to learn the flow map of the dynamical system.
Solving Ordinary Differential Equations I
Ernst Hairer, S.E. Nörsett, and G. Wanner · 1987
Earlier work this paper cites.
Lectures on Phase Transitions and the Renormalization Group
N Goldenfeld · 1992
Earlier work this paper cites.
Q-learning
Christopher JCH Watkins and Peter Dayan · 1992
Earlier work this paper cites.
Nonlinear Control Systems
Alberto Isidori · 1995
Earlier work this paper cites.
Policy gradient methods for reinforcement learning with function approximation
Richard S. Sutton, David McAllester, Satinder Singh, and Yishay Mansour · 1999
Earlier work this paper cites.
An introduction to game theory
Martin J Osborne et al · 2004
Earlier work this paper cites.
Problem reduction, renormalization and memory
Alexandre J Chorin and Panagiotis Stinis · 2007
Earlier work this paper cites.
Reward design via online gradient ascent
Jonathan Sorg, Richard L Lewis, and Satinder P Singh · 2010
Earlier work this paper cites.
A survey of actor-critic reinforcement learning: Standard and natural policy gradients
Ivo Grondman, Lucian Busoniu, Gabriel AD Lopes, and Robert Babuska · 2012
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Cited alongside, same era.
Deterministic policy gradient algorithms
David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, and Martin Riedmiller · 2014
Cited alongside, same era.
Nonparametric forecasting of low-dimensional dynamical systems
Tyrus Berry, Dimitrios Giannakis, and John Harlim · 2015
Cited alongside, same era.
Continuous control with deep reinforcement learning
Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2015
Cited alongside, same era.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis · 2015
Cited alongside, same era.
Physics-constrained, data-driven discovery of coarse-grained dynamics
L. Felsberger and P.S. Koutsourelakis · 2018
Later among the works it cites.
Solving high-dimensional partial differential equations using deep learning
Jiequn Han, Arnulf Jentzen, and Weinan E · 2018
Later among the works it cites.
Randomly distributed embedding making short-term high-dimensional data predictable
Huanfei Ma, Siyang Leng, Kazuyuki Aihara, Wei Lin, and Luonan Chen · 2018
Later among the works it cites.
Numerical Gaussian processes for time-dependent and nonlinear partial differential equations
Maziar Raissi, Paris Perdikaris, and George Karniadakis · 2018
Later among the works it cites.
DGM: A deep learning algorithm for solving partial differential equations
Justin Sirignano and Konstantinos Spiliopoulos · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Deep learning for reward design to improve monte carlo tree search in atari games
Xiaoxiao Guo, Satinder Singh, Richard Lewis, and Honglak Lee · 2016
Cited alongside, same era.
Connecting generative adversarial networks and actor-critic methods
David Pfau and Oriol Vinyals · 2016
Cited alongside, same era.
Towards principled methods for training Generative Adversarial Networks
Martin Arjovsky and Léon Bottou · 2017
Cited alongside, same era.
Neural ordinary differential equations
Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, and David Duvenaud · 2018
Cited alongside, same era.
P. Stinis, T. Hagge, A. M. Tartakovsky, and E. Young · 2018
Later among the works it cites.
Data-assisted reduced-order modeling of extreme events in complex dynamical systems
ZY Wan, P Vlachas, P Koumoutsakos, and T Sapsis · 2018
Later among the works it cites.
Workshop report on basic research needs for scientific machine learning: Core technologies for artificial intelligence
Nathan Baker, Frank Alexander, Timo Bremer, Aric Hagberg, Yannis Kevrekidis, Habib Najm, Manish Parashar, Abani Patra, James Sethian, Stefan Wild, and Karen Willcox · 2019
Closest in time.