Physics-informed generative U-Nets evolve and super-resolve fuzzy dark matter fields under Schrödinger–Poisson constraints with far less supervised data than pure data-driven baselines.
A Hybrid Scheme for Fuzzy Dark Matter Simulations Combining the Schr\"odinger and Hamilton-Jacobi-Madelung Equations
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abstract
This paper introduces a hybrid numerical scheme for the fuzzy dark matter model: It combines a wave-based approach to solve the Schr\"odinger equation using Fourier continuations with Gram polynomials and a fluid-based approach to solve the Hamilton-Jacobi-Madelung equations. This hybrid scheme facilitates zoom-in simulations for cosmological volumes beyond the capabilities of wave-based solvers alone and accurately simulates the full nonlinear dynamics of fuzzy dark matter. We detail the implementation of a Hamilton-Jacobi-Madelung solver, the methodology for phase matching at fluid-wave boundaries, the development of a local pseudospectral wave solver based on Fourier continuations, new grid refinement criteria for both fluid and wave solvers, an interpolation algorithm based on Fourier continuations, and the integration of these building blocks into the adaptive mesh refinement code GAMER. The superiority of the scheme is demonstrated through various performance and accuracy tests, tracking the linear power spectrum evolution in a 10 Mpc/h box, and a hybrid cosmological simulation in a 5.6 Mpc/h box. The corresponding code is published as part of the GAMER project on https://github.com/gamer-project/gamer.
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Cosmo-SPINN: Fuzzy Dark Matter Simulations with Physics-Informed Generative Networks
Physics-informed generative U-Nets evolve and super-resolve fuzzy dark matter fields under Schrödinger–Poisson constraints with far less supervised data than pure data-driven baselines.