DeepOperatorML
We propose that the use of operator learning schemes (DeepONet, FNO) can vastly improve the computational cost of solving boundary value problems on unbounded domains when compared to classical methods using numerical integration. One such example is Rayleigh wave propagation on unbounded media. The project’s original goal was to reduce the cost of the Boundary Element Method using Operator Learning, but I later extended it for solving any physical system governed by PDEs. Currently, the repository consists of a python library written during my Master’s thesis that proposes a framework for solving data-driven scientific machine learning problems using several implemented architectures (a wide variety of DeepONets and FNOs).
