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Mar 18, 2021 · Pengzhan Jin, Guofei Pang, Zhongqiang Zhang & George Em Karniadakis. Nature Machine Intelligence 3 , 218–229 ( 2021) Cite this article. 33k Accesses. 693 Citations. 178 Altmetric. Metrics....
- Lu Lu, Pengzhan Jin, Pengzhan Jin, Guofei Pang, Zhongqiang Zhang, George Em Karniadakis
- 2021
The source code for the paper L. Lu, P. Jin, G. Pang, Z. Zhang, & G. E. Karniadakis. Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators. Nature Machine Intelligence, 3, 218-229, 2021.
Oct 8, 2019 · Authors: Lu Lu, Pengzhan Jin, Guofei Pang, Zhongqiang Zhang, George Em Karniadakis Download a PDF of the paper titled DeepONet: Learning nonlinear operators based on the universal approximation theorem of operators, by Lu Lu and 4 other authors
- Lu Lu, Pengzhan Jin, Guofei Pang, Zhongqiang Zhang, George Em Karniadakis
- 2019
To realize this theorem, we design a new NN with small generalization error, the deep operator network (DeepONet), consisting of a NN for encoding the discrete input function space (branch net) and another NN for encoding the domain of the output functions (trunk net).
Mar 18, 2021 · We design a new network with small generalization error, the deep operator network (DeepONet), which consists of a DNN for encoding the discrete input function space (branch net) and another DNN for encoding the domain of the output functions (trunk net).
Jul 9, 2013 · The porous structure of the DNA hydrogel allows nutrients and waste to pass through, leading to a cell viability as high as 98%. The design provides a general method to culture, monitor, and manipulate single cells, and has potential applications in cell patterning and studying cell communication.
Mar 1, 2021 · Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators. Journal Article · Mon Mar 01 00:00:00 EST 2021 · Nature Machine Intelligence. DOI: https://doi.org/10.1038/s42256-021-00302-5 · OSTI ID: 1853304.
