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

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 $h_0$, 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}.

arXiv comment10 pages, 6 figures, 2 tables; ApJL accepted

References in corpus (19)

• Array Programming with NumPy
• DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators
• Planet-disk interaction and orbital evolution
• A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks
• FARGO: a fast eulerian transport algorithm for differentially rotating disks
• The Disk Substructures at High Angular Resolution Project (DSHARP): VII. The Planet-Disk Interactions Interpretation
• Observational Signatures of Planets in Protoplanetary Disks I: Gaps Opened by Single and Multiple Young Planets in Disks
• FARGO3D: A new GPU-oriented MHD code
• How Empty are Disk Gaps Opened by Giant Planets?
• Mass constraint for a planet in a protoplanetary disk from the gap width
• What is the Mass of a Gap-Opening Planet?
• Inferring Planet Mass from Spiral Structures in Protoplanetary Disks
• New constraints on turbulence and embedded planet mass in the HD 163296 disk from planet-disk hydrodynamic simulations
• Molecules with ALMA at Planet-forming Scales (MAPS). A Circumplanetary Disk Candidate in Molecular Line Emission in the AS 209 Disk
• Gaps and Rings in Protoplanetary Disks with Realistic Thermodynamics: The Critical Role of In-Plane Radiation Transport
• Properties of Density and Velocity Gaps Induced by a Planet in a Protoplanetary Disk
• PGNets: Planet mass prediction using convolutional neural networks for radio continuum observations of protoplanetary disks
• DPNNet-2.0 Part I: Finding hidden planets from simulated images of protoplanetary disk gaps
• A Machine Learning model to infer planet masses from gaps observed in protoplanetary disks

Cited by in corpus (7)

• A Mathematical Guide to Operator Learning
• DBNets: A publicly available deep learning tool to measure the masses of young planets in dusty protoplanetary discs
• Quantum DeepONet: Neural operators accelerated by quantum computing
• DBNets2.0: simulation-based inference for planet-induced dust substructures in protoplanetary discs
• Surrogate Modeling of Landau Damping with Deep Operator Networks
• Searching for planet-driven dust spirals in ALMA visibilities
• exoALMA XX: Tomographic Detection of Embedded Planets in Protoplanetary Disks