deeponet作者相关三篇论文链接（理论基础、实用拓展、外推）

第一篇  理论基础（DeepXDE<=0.11.2,低版本）：

https://github.com/lululxvi/deeponet

nature题目及其链接：Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators | Nature Machine Intelligence

arxiv:https://arxiv.org/abs/1910.03193

arxiv题目：DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators

DeepONet：基于算子的普遍逼近定理学习用于识别微分方程的非线性算子

第二篇  实用拓展(DeepXDE>0.11.2,高版本)：

题目：基于 FAIR 数据对两个神经算子（及实际扩展）进行全面、公平的比较

https://arxiv.org/abs/2111.05512

https://github.com/lululxvi/deeponet

第三篇  外推

题目：Reliable extrapolation of deep neural operators informed by physics or sparse observation基于物理或稀疏观测的深度神经算子的可靠外推

论文链接：https://arxiv.org/abs/2212.06347

博主自己电脑上的机翻的中文翻译：[2212.06347] Reliable extrapolation of deep neural operators informed by physics or sparse observations

code:GitHub - lu-group/deeponet-extrapolation: DeepONet extrapolation 

                        
原文链接：https://blog.csdn.net/weixin_44162814/article/details/143734531