史上最全DL Beyond CS 論文列表

www.labxing.com

目前主要包括：

1. Dynamic Perspective Of DL
2. DL+Numerical PDE
3. DL+Physics

這裡摘錄一些：

DL+Numerical PDE

• Jonathan Tompson, Kristofer Schlachter, Pablo Sprechmann, Ken PerlinACCELERATING EULERIAN FLUID SIMULATION WITH CONVOLUTIONAL NETWORKSICLR2017 workshop version:link
• Jiequn Han, Weinan E.Deep Learning Approximation for Stochastic Control Problems arxivHoping approximation by neural network can overcome the curse of dimensionality to solve PDE in high dimensional space.
• Jiequn Han, Arnulf Jentzen, Weinan E.Overcoming the curse of dimensionality: Solving high-dimensional partial differential equations using deep learning arxivlonger version.
• J.Nagoor Kani, Ahmed H. Elsheikh.DR-RNN: A deep residual recurrent neural network for model reduction arXivDesigned a physic based RNN with residual connection to do model reduction.(Reduce the dimension for a dynamic.)
• Weinan E, Bing YuThe Deep Ritz method: A deep learning-based numerical algorithm for solving variational problems. arXiv:1710.00211
• Christian Beck, Weinan E, Arnulf Jentzen Machine learning approximation algorithms for high-dimensional fully nonlinear partial differential equations and second-order backward stochastic differential equations.
• Masaaki Fujii, Akihiko Takahashi, Masayuki Takahashi.Asymptotic Expansion as Prior Knowledge in Deep Learning Method for high dimensional BSDEs
• Zichao long, Yiping Lu, Xianzhong Ma, Bin Dong. PDE-Net:Learning PDEs From Data
• Jens Berg, Kaj Nystr?mA unified deep artificial neural network approach to partial differential equations in complex geometriesarXiv
• Emmanuel de Bezenac, Arthur Pajot, Patrick Gallinari Deep Learning for Physical Processes: Incorporating Prior Scientific KnowledgearXiv
• Ronan Fablet, Said Ouala, Cedric Herzet Bilinear residual Neural Network for the identification and forecasting of dynamical systems arXiv
• Y. Khoo, J. Lu, and L. Ying. Solving parametric PDE problems with artificial neural networks. pdf
• Linfeng Zhang, Han Wang, Weinan E Reinforced dynamics for enhanced sampling in large atomic and molecular systems. I. Basic MethodologyarXiv
• Maziar Raissi, Paris Perdikaris, George Em Karniadakis Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations arXiv2 arXiv(part2)

DL+Physics

• "A sequential sampling strategy for extreme event statistics in nonlinear dynamical systems", Mustafa A. Mohamad, Themistoklis P. Sapsis, arXiv: 1804.07240, 4/2018
• "Method to solve quantum few-body problems with artificial neural networks", Hiroki Saito, arXiv: 1804.06521, 4/2018
• "Classicalization Clearly: Quantum Transition into States of Maximal Memory Storage Capacity", Gia Dvali, arXiv: 1804.06154, 4/2018
• "Machine learning of phase transitions in the percolation and XY models", Wanzhou Zhang, Jiayu Liu, Tzu-Chieh Wei, arXiv: 1804.02709, 4/2018
• "Complex energy landscapes in spiked-tensor and simple glassy models: ruggedness, arrangements of local minima and phase transitions", Valentina Ros, Gerard Ben Arous, Giulio Biroli, Chiara Cammarota, arXiv: 1804.02686, 4/2018
• "Smallest Neural Network to Learn the Ising Criticality", Dongkyu Kim, Dong-Hee Kim, arXiv: 1804.02171, 4/2018
• "Learning quantum models from quantum or classical data", Hilbert J Kappen, arXiv: 1803.11278, 3/2018
• "Deep Learning Phase Segregation", Amir Barati Farimani, Joseph Gomes, Rishi Sharma, Franklin L. Lee, Vijay S. Pande, arXiv: 1803.08993, 3/2018
• "A high-bias, low-variance introduction to Machine Learning for physicists", Pankaj Mehta, Marin Bukov, Ching-Hao Wang, Alexandre G. R. Day, Clint Richardson, Charles K. Fisher, David J. Schwab, arXiv: 1803.08823, 3/2018
• "Parameter diagnostics of phases and phase transition learning by neural networks", Philippe Suchsland, Stefan Wessel, arXiv: 1802.09876, 2/2018
• "Advantages of versatile neural-network decoding for topological codes", Nishad Maskara, Aleksander Kubica, Tomas Jochym-OConnor, arXiv: 1802.08680, 2/2018
• "Reinforcement Learning with Neural Networks for Quantum Feedback", Thomas F?sel, Petru Tighineanu, Talitha Weiss, Florian Marquardt, arXiv: 1802.05267, 2/2018
• "Online Learning of Quantum States", Scott Aaronson, Xinyi Chen, Elad Hazan, Ashwin Nayak, arXiv: 1802.09025, 2/2018
• "Deep neural decoders for near term fault-tolerant experiments", Christopher Chamberland, Pooya Ronagh, arXiv: 1802.06441, 2/2018
• "Neural Network Renormalization Group", Shuo-Hui Li, Lei Wang, arXiv: 1802.02840, 2/2018
• "Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification", Rohit Tripathy, Ilias Bilionis, arXiv: 1802.00850, 2/2018
• "Experimentally detecting a quantum change point via Bayesian inference", Shang Yu, Chang-Jiang Huang, Jian-Shun Tang, Zhih-Ahn Jia, Yi-Tao Wang, Zhi-Jin Ke, Wei Liu, Xiao Liu, Zong-Quan Zhou, Ze-Di Cheng, Jin-Shi Xu, Yu-Chun Wu, Yuan-Yuan Zhao, Guo-Yong Xiang, Chuan-Feng Li, Guang-Can Guo, Gael Sentís, Ramon Mu?oz-Tapia, arXiv: 1801.07508, 1/2018
• "Generative Models for Stochastic Processes Using Convolutional Neural Networks", Fernando Fernandes Neto, arXiv: 1801.03523, 1/2018

參考:

[1] ? 2prime (@陸一平-北京大學-數學 )

[2] 〈 physics | machine learning 〉