PPDONet: Deep Operator Networks for Fast Prediction of Steady-State Solutions in Disk-Planet Systems

TL;DR

PPDONet is a neural network-based tool that rapidly predicts steady-state solutions of disk-planet interactions in protoplanetary disks, enabling real-time analysis with high accuracy.

Contribution

This work introduces PPDONet, the first application of Deep Operator Networks to model complex disk-planet systems efficiently.

Findings

Predicts disk-planet interaction outcomes in less than a second

Accurately maps system parameters to steady-state solutions

Available as an open-source tool

Abstract

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 $\alpha$, the disk aspect ratio $h_\mathrm{0}$, 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}.