Theory-guided hard constraint projection (HCP): a knowledge-based data-driven scientific machine learning method

[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.