Abstract As the Neural Network framework(Physics-informed Neural Network) is introduced to solve PDEs and dynamical systems, solutions of differential equations are learned from data, and this could replace or improve the conventional numerical solvers. Still, there are limitations on this “learning functions”. A new framework of “learning operators” came up in order to cure the limitations, and in specific, DeepONet provided a Neural Network architecture for learning operators which map functions to functions. This talk will show the mechanism and deeds of DeepONet(2020) and mention its follow-up studies.

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