PPDONet: Deep Operator Networks for Fast Prediction of Steady-state Solutions in Disk–Planet Systems
| Autor: | Shunyuan Mao, Ruobing Dong, Lu Lu, Kwang Moo Yi, Sifan Wang, Paris Perdikaris |
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| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | The Astrophysical Journal Letters, Vol 950, Iss 2, p L12 (2023) |
| Druh dokumentu: | article |
| ISSN: | 2041-8213 2041-8205 |
| DOI: | 10.3847/2041-8213/acd77f |
| Popis: | 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, a class of neural networks capable of learning nonlinear 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 h _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 https://github.com/smao-astro/PPDONet. |
| Databáze: | Directory of Open Access Journals |
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