Kavli Affiliate: Lile Wang
| First 5 Authors: Shunyuan Mao, Shunyuan Mao, , ,
| Summary:
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.
| Search Query: ArXiv Query: search_query=au:”Lile Wang”&id_list=&start=0&max_results=3