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Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators
Lu Lu, Pengzhan Jin, Guofei Pang, Zhongqiang Zhang, George Em Karniadakis
Massachusetts Institute of Technology Chinese Academy of Sciences Brown University Academy of Mathematics and Systems Science
来源Nature Machine Intelligence
年份2021
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学术脉络
学科主题
物理Model Reduction and Neural Networks
Neural Networks and Applications · Gaussian Processes and Bayesian Inference
参考文献 35
A hybrid neural network‐first principles approach to process modeling
被引 982Dimitris C. Psichogios, Lyle Ungar · AIChE Journal · 1992
Identification of distributed parameter systems: A neural net based approach
被引 244Raúl González‐García, Ramiro Rico-Martı́nez, Ioannis G. Kevrekidis · Computers & Chemical Engineering · 1998
DISCRETE- vs. CONTINUOUS-TIME NONLINEAR SIGNAL PROCESSING OF Cu ELECTRODISSOLUTION DATA
被引 144Ramiro Rico-Martı́nez, Katharina Krischer, Ioannis G. Kevrekidis · Chemical Engineering Communications · 1992
此处列出前 3 条
引用本文 3,663
Physics-informed machine learning
被引 8,760George Em Karniadakis, Ioannis G. Kevrekidis, Lu Lu · Nature Reviews Physics · 2021
Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next
被引 2,872Salvatore Cuomo, Vincenzo Schiano Di Cola, Fabio Giampaolo · Journal of Scientific Computing · 2022
On the Opportunities and Risks of Foundation Models
被引 2,677Rishi Bommasani, Drew A. Hudson, Ehsan Adeli · arXiv (Cornell University) · 2021
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