GRINN: A Physics-Informed Neural Network for solving hydrodynamic systems in the presence of self-gravity
This addresses the problem of efficiently simulating complex astrophysical flows for researchers, offering a novel mesh-free approach that is incremental in applying PINNs to a specific domain.
The paper tackles the challenge of modeling 3D self-gravitating hydrodynamic systems, such as those in astrophysics, by introducing GRINN, a physics-informed neural network. It achieves results matching linear analytic solutions within 1% and grid code solutions within 5%, with computation time not scaling with dimensions and being an order of magnitude faster than grid codes in 3D.
Modeling self-gravitating gas flows is essential to answering many fundamental questions in astrophysics. This spans many topics including planet-forming disks, star-forming clouds, galaxy formation, and the development of large-scale structures in the Universe. However, the nonlinear interaction between gravity and fluid dynamics offers a formidable challenge to solving the resulting time-dependent partial differential equations (PDEs) in three dimensions (3D). By leveraging the universal approximation capabilities of a neural network within a mesh-free framework, physics informed neural networks (PINNs) offer a new way of addressing this challenge. We introduce the gravity-informed neural network (GRINN), a PINN-based code, to simulate 3D self-gravitating hydrodynamic systems. Here, we specifically study gravitational instability and wave propagation in an isothermal gas. Our results match a linear analytic solution to within 1\% in the linear regime and a conventional grid code solution to within 5\% as the disturbance grows into the nonlinear regime. We find that the computation time of the GRINN does not scale with the number of dimensions. This is in contrast to the scaling of the grid-based code for the hydrodynamic and self-gravity calculations as the number of dimensions is increased. Our results show that the GRINN computation time is longer than the grid code in one- and two- dimensional calculations but is an order of magnitude lesser than the grid code in 3D with similar accuracy. Physics-informed neural networks like GRINN thus show promise for advancing our ability to model 3D astrophysical flows.