Speaker
Description
Physics-Informed Neural Networks (PINNs) have emerged as a powerful tool for solving differen-
tial equations by integrating physical laws into the learning process. This work leverages PINNs to
simulate gravitational collapse, a critical phenomenon in astrophysics and cosmology. We introduce
the Schr¨odinger-Poisson informed neural network (SPINN) which solve nonlinear Schr¨odinger-Poisson
(SP) equations to simulate the gravitational collapse of Fuzzy Dark Matter (FDM) in both 1D and
3D settings. Results demonstrate accurate predictions of key metrics such as mass conservation, den-
sity profiles, and structure suppression, validating against known analytical or numerical benchmarks.
This work highlights the potential of PINNs for efficient, possibly scalable modeling of FDM and other
astrophysical systems, overcoming the challenges faced by traditional numerical solvers due to the
non-linearity of the involved equations and the necessity to resolve multi-scale phenomena especially
resolving the fine wave features of FDM on cosmological scales.
