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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Maziar Raissi, Paris Perdikaris, George Em Karniadakis
Brown University University of Pennsylvania
来源Journal of Computational Physics
年份2018
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学术脉络
学科主题
物理Model Reduction and Neural Networks
Fluid Dynamics and Turbulent Flows · Meteorological Phenomena and Simulations
参考文献 64
Spectral/hp Element Methods for Computational Fluid Dynamics
被引 1,478George Em Karniadakis, Spencer J. Sherwin · Oxford University Press eBooks · 2005
Neural Networks for Signal Processing IV : proceedings of the 1994 IEEE Workshop
被引 4J.A. Vlontzos, Jenq–Neng Hwang, Elizabeth J. Wilson · Medical Entomology and Zoology · 1994
Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
被引 3,206Babak Alipanahi, Andrew Delong, Matthew T. Weirauch · Nature Biotechnology · 2015
此处列出前 3 条
引用本文 18,467
Physics-informed machine learning
被引 7,395George Em Karniadakis, Ioannis G. Kevrekidis, Lu Lu · Nature Reviews Physics · 2021
Machine Learning for Fluid Mechanics
被引 2,759Steven L. Brunton, Bernd R. Noack, Petros Koumoutsakos · Annual Review of Fluid Mechanics · 2019
Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next
被引 2,541Salvatore Cuomo, Vincenzo Schiano Di Cola, Fabio Giampaolo · Journal of Scientific Computing · 2022
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