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pmwd: A Differentiable Cosmological Particle-Mesh $N$-body Library
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The formation of the large-scale structure, the evolution and distribution of galaxies, quasars, and dark matter on cosmological scales, requires numerical simulations. Differentiable simulations provide gradients of the cosmological parameters, that can accelerate the extraction of physical information from statistical analyses of observational data. The deep learning revolution has brought not only myriad powerful neural networks, but also breakthroughs including automatic differentiation (AD) tools and computational accelerators like GPUs, facilitating forward modeling of the Universe with differentiable simulations. Because AD needs to save the whole forward evolution history to backpropagate gradients, current differentiable cosmological simulations are limited by memory. Using the adjoint method, with reverse time integration to reconstruct the evolution history, we develop a differentiable cosmological particle-mesh (PM) simulation library pmwd (particle-mesh with derivatives) with a low memory cost. Based on the powerful AD library JAX, pmwd is fully differentiable, and is highly performant on GPUs.
Forward citations
Cited by 6 Pith papers
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Emulating Cosmic Structure Formation with a Lagrangian Neural Cellular Automaton
A ~4,000-parameter recurrent local network correcting the Zeldovich approximation reaches percent-level matter power-spectrum accuracy at k≲0.5 h/Mpc at z=0, matching larger U-Net emulators on Quijote N-body tests.
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From Dark Matter to Galaxies: Halo-Free Mock Generation via Conditional Point-Cloud Diffusion
A conditional point-cloud diffusion model trained on IllustrisTNG generates galaxy mocks with SFR and stellar mass directly from dark-matter density fields, bypassing halo identification.
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Per Astronomix ad Astra: High-Order Differentiable (Magneto)hydrodynamics with Energy-Conserving Self-Gravity
A new differentiable MHD+self-gravity simulator combines a fifth-order finite-difference WENO scheme with a fourth-order semi-discretely energy-conserving self-gravity module.
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Diffhalos: A Generative Model of Cosmological Lightcones of Dark Matter Halos
Diffhalos generates statistically accurate Monte-Carlo and quasi-Monte-Carlo lightcones of halos, subhalos and Diffmah mass-assembly histories, enabling autodiff gradients of the mass functions.
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DISCO-DJ II: a differentiable particle-mesh code for cosmology
A GPU-accelerated, differentiable particle-mesh N-body code achieves per-cent-level power-spectrum accuracy with few time steps and recovers sigma_8 plus initial conditions from a noisy mock field.
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Fisher Score Matching for Simulation-Based Forecasting and Inference
A neural network trained to predict latent parameter scores can approximate the Fisher score and support Fisher forecasting and gradient-based Bayesian inference from simulations.
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