We develop a tool, which we name Protoplanetary Disk Operator Network (PPDONet), that can predict the solution of disk-planet interactions in protoplanetary disks in real-time. We base our tool on Deep Operator Networks (DeepONets), a class of neural networks capable of learning non-linear operators to represent deterministic and stochastic differential equations. With PPDONet we map three scalar parameters in a disk-planet system -- the Shakura \& Sunyaev viscosity α, the disk aspect ratio h0, and the planet-star mass ratio q -- to steady-state solutions of the disk surface density, radial velocity, and azimuthal velocity. We demonstrate the accuracy of the PPDONet solutions using a comprehensive set of tests. Our tool is able to predict the outcome of disk-planet interaction for one system in less than a second on a laptop. A public implementation of PPDONet is available at \url{https://github.com/smao-astro/PPDONet}.
@article{arxiv.2305.11111,
title = {PPDONet: Deep Operator Networks for Fast Prediction of Steady-State Solutions in Disk-Planet Systems},
author = {Shunyuan Mao and Ruobing Dong and Lu Lu and Kwang Moo Yi and Sifan Wang and Paris Perdikaris},
journal= {arXiv preprint arXiv:2305.11111},
year = {2023}
}