Theory-guided hard constraint projection (HCP): a knowledge-based data-driven scientific machine learning method
暂无分享,去创建一个
Yuntian Chen | Dongxiao Zhang | Junsheng Zeng | Nanzhe Wang | Dou Huang | Jinyue Yan | Haoran Zhang | Dongxiao Zhang | Nanzhe Wang | Jinyue Yan | Dou Huang | Yuntian Chen | Junsheng Zeng | Haoran Zhang | Haoran Zhang
[1] Eric Darve,et al. Physics Constrained Learning for Data-driven Inverse Modeling from Sparse Observations , 2020, J. Comput. Phys..
[2] Pierre Baldi,et al. Enforcing Analytic Constraints in Neural Networks Emulating Physical Systems. , 2019, Physical review letters.
[3] Bo Zhang,et al. Toward the third generation artificial intelligence , 2020, Science China Information Sciences.
[4] Muhammed Sit,et al. A Comprehensive Review of Deep Learning Applications in Hydrology and Water Resources , 2020, Water science and technology : a journal of the International Association on Water Pollution Research.
[5] Dongxiao Zhang,et al. Deep Learning of Dynamic Subsurface Flow via Theory-guided Generative Adversarial Network , 2020, ArXiv.
[6] Dongxia Zhang,et al. Deep Learning Based Forecasting of Photovoltaic Power Generation via Theory-guided LSTM , 2020 .
[7] P. Wriggers,et al. The Neural Particle Method - An Updated Lagrangian Physics Informed Neural Network for Computational Fluid Dynamics , 2020, Computer Methods in Applied Mechanics and Engineering.
[8] Yuntian Chen,et al. Physics-constrained indirect supervised learning , 2020, ArXiv.
[9] Michael Chertkov,et al. Embedding Hard Physical Constraints in Neural Network Coarse-Graining of 3D Turbulence , 2020, 2002.00021.
[10] Dongxiao Zhang,et al. Deep Learning of Subsurface Flow via Theory-guided Neural Network , 2019, Journal of Hydrology.
[11] Timon Rabczuk,et al. An Energy Approach to the Solution of Partial Differential Equations in Computational Mechanics via Machine Learning: Concepts, Implementation and Applications , 2019, Computer Methods in Applied Mechanics and Engineering.
[12] R. Moradi,et al. Application of Neural Network for estimation of heat transfer treatment of Al2O3-H2O nanofluid through a channel , 2019, Computer Methods in Applied Mechanics and Engineering.
[13] Paris Perdikaris,et al. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations , 2019, J. Comput. Phys..
[14] Habib N. Najm,et al. Workshop Report on Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial Intelligence , 2018 .
[15] Bin Dong,et al. PDE-Net: Learning PDEs from Data , 2017, ICML.
[16] Anuj Karpatne,et al. Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling , 2017, ArXiv.
[17] Hang Su,et al. Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples , 2017, ArXiv.
[18] Nagiza F. Samatova,et al. Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data , 2016, IEEE Transactions on Knowledge and Data Engineering.
[19] Max Welling,et al. Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.
[20] Trevor Darrell,et al. Constrained Convolutional Neural Networks for Weakly Supervised Segmentation , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[21] Liqun Qi,et al. A novel neural network for variational inequalities with linear and nonlinear constraints , 2005, IEEE Transactions on Neural Networks.
[22] Shu-Hsien Liao,et al. Expert system methodologies and applications - a decade review from 1995 to 2004 , 2005, Expert Syst. Appl..
[23] Kunihiko Fukushima,et al. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position , 1980, Biological Cybernetics.
[24] Jun Wang,et al. A projection neural network and its application to constrained optimization problems , 2002 .
[25] Kok-Kwang Phoon,et al. Simulation of second-order processes using Karhunen–Loeve expansion , 2002 .
[26] Murray Campbell,et al. Deep Blue , 2002, Artif. Intell..
[27] Dieter Fensel,et al. Knowledge Engineering: Principles and Methods , 1998, Data Knowl. Eng..
[28] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[29] François Laporte. On the design of an expert system guide for the use of scientific software , 1989 .
[30] Bruce G. Buchanan,et al. The MYCIN Experiments of the Stanford Heuristic Programming Project , 1985 .
[31] J J Hopfield,et al. Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.
[32] Joshua Lederberg,et al. Applications of Artificial Intelligence for Organic Chemistry: The DENDRAL Project , 1980 .
[33] Keinosuke Fukunaga,et al. Application of the Karhunen-Loève Expansion to Feature Selection and Ordering , 1970, IEEE Trans. Computers.
[34] F ROSENBLATT,et al. The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.