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Mar 18, 2021 · Article. Published: 18 March 2021. Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators. Lu Lu. Pengzhan Jin. Guofei Pang. Zhongqiang Zhang....
- 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
- arXiv:1910.03193 [cs.LG]
- 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).
Pengzhan Jin . Guofei Pang . Zhongqiang Zhang . George Em Karniadakis. Keyword (s): Approximation Theorem . Universal Approximation . Nonlinear Operators. Download Full-text. Related Documents. Cited By. References. A Universal Approximation Theorem for Gaussian-Gated Mixture of Experts Models. SSRN Electronic Journal . 10.2139/ssrn.2946964
Oct 8, 2019 · TLDR. A new deep neural network called DeepONet can lean various mathematical operators with small generalization error and can learn various explicit operators, such as integrals and fractional Laplacians, as well as implicit operators that represent deterministic and stochastic differential equations. Expand.
