SPINN: Advancing Cosmological Simulations of Fuzzy Dark Matter with Physics Informed Neural Networks

arXiv:2506.02957 · astro-ph.CO · Submitted 2025-06-03 · Read on arXiv

astro-ph.CO

Submitted: 2025-06-03

Updated: 2025-06-03

Terminology

• Activation Function — 13× in this paper · explained in 1 episode
• Accuracy — 9× in this paper · explained in 1 episode
• Adam — 7× in this paper · explained in 2 episodes
• Fuzzy Dark Matter — 7× in this paper
• Loss Function — 6× in this paper · explained in 2 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• PDE residual — 3× in this paper · explained in 2 episodes
• Physics-Informed Neural Networks (PINNs) — 3× in this paper · explained in 6 episodes
• Semi-supervised Learning — 3× in this paper · explained in 4 episodes
• Core — 2× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Inverse Problem — 2× in this paper · explained in 2 episodes
• Mean Squared Error — 2× in this paper
• Abundance — 1× in this paper · explained in 1 episode
• Baryonic Feedback — 1× in this paper · explained in 3 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Convolutional Neural Networks (CNNs) — 1× in this paper · explained in 2 episodes
• Fast Fourier Transforms — 1× in this paper
• Generalizability — 1× in this paper · explained in 6 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Higher-order derivatives — 1× in this paper · explained in 2 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Studies of the Cosmic Microwave Background — 1× in this paper
• Uncertainty Quantification — 1× in this paper · explained in 8 episodes

Sources

• A hands-on introduction to Physics-Informed Neural Networks for solving partial differential equations with benchmark tests taken from astrophysics and plasma physics
• A Tutorial on the Use of Physics-Informed Neural Networks to Compute the Spectrum of Quantum Systems
• dm2gal: Mapping Dark Matter to Galaxies with Neural Networks
• DPM: A Novel Training Method for Physics-Informed Neural Networks in Extrapolation
• Adam: A Method for Stochastic Optimization
• PINION: Physics-informed neural network for accelerating radiative transfer simulations for cosmic reionization
• Physics informed Neural Networks applied to the description of wave-particle resonance in kinetic simulations of fusion plasmas
• A Hybrid Scheme for Fuzzy Dark Matter Simulations Combining the Schr\"odinger and Hamilton-Jacobi-Madelung Equations
• Axion Cosmology
• PinnDE: Physics-Informed Neural Networks for Solving Differential Equations
• Solitons in the dark: non-linear structure formation with fuzzy dark matter
• DAE-PINN: A Physics-Informed Neural Network Model for Simulating Differential-Algebraic Equations with Application to Power Networks
• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• On the Spectral Bias of Neural Networks
• Hidden Fluid Mechanics: A Navier-Stokes Informed Deep Learning Framework for Assimilating Flow Visualization Data
• Challenges in Training PINNs: A Loss Landscape Perspective
• Physics-Informed Kolmogorov-Arnold Networks for Power System Dynamics
• Implicit Neural Representations with Periodic Activation Functions
• Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains
• When and why PINNs fail to train: A neural tangent kernel perspective

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