Lu Lu and Paris Perdikaris Publish a New Paper

The paper entitled “PPDONet: Deep Operator Networks for Fast Prediction of Steady-State Solutions in Disk-Planet Systems” by Shunyuan Mao, Ruobing Dong, Lu Lu, Kwang Moo Yi, Sifan Wang, and Paris Perdikaris was published to arXiv on May 18th, 2023. Below, you can find the paper’s abstract and a link to the paper.


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

Link to Paper

You can find the paper here.