Neural Networks as Surrogate Solvers for Time-Dependent Accretion Disk Dynamics

• URL: http://arxiv.org/abs/2509.20447v1
• Date: Wed, 24 Sep 2025 18:01:04 GMT
• Title: Neural Networks as Surrogate Solvers for Time-Dependent Accretion Disk Dynamics
• Authors: Shunyuan Mao, Weiqi Wang, Sifan Wang, Ruobing Dong, Lu Lu, Kwang Moo Yi, Paris Perdikaris, Andrea Isella, Sébastien Fabbro, Lile Wang,
• Abstract summary: Accretion disks are ubiquitous in astrophysics, appearing in diverse environments from planet-forming systems to X-ray binaries and active galactic nuclei.<n>Traditionally, modeling their dynamics requires computationally intensive (magneto)hydrodynamic simulations.<n>Recently, Physics-Informed Neural Networks (PINNs) have emerged as a promising alternative.<n>We for the first time demonstrate PINNs for solving the two-dimensional, time-dependent hydrodynamics of non-self-gravitating accretion disks.
• Score: 25.142937286955547
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accretion disks are ubiquitous in astrophysics, appearing in diverse environments from planet-forming systems to X-ray binaries and active galactic nuclei. Traditionally, modeling their dynamics requires computationally intensive (magneto)hydrodynamic simulations. Recently, Physics-Informed Neural Networks (PINNs) have emerged as a promising alternative. This approach trains neural networks directly on physical laws without requiring data. We for the first time demonstrate PINNs for solving the two-dimensional, time-dependent hydrodynamics of non-self-gravitating accretion disks. Our models provide solutions at arbitrary times and locations within the training domain, and successfully reproduce key physical phenomena, including the excitation and propagation of spiral density waves and gap formation from disk-companion interactions. Notably, the boundary-free approach enabled by PINNs naturally eliminates the spurious wave reflections at disk edges, which are challenging to suppress in numerical simulations. These results highlight how advanced machine learning techniques can enable physics-driven, data-free modeling of complex astrophysical systems, potentially offering an alternative to traditional numerical simulations in the future.

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