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pmwd: A Differentiable Cosmological Particle-Mesh N-body Library
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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 5 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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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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The Degeneracy Distillery
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Towards Practical Field-Level Inference for Weak Lensing
Field-level inference from weak lensing maps yields significantly tighter cosmological constraints than power-spectrum analysis when using the same forward-modeling pipeline, especially on small scales.
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Cosmological Analysis with Calibrated Neural Quantile Estimation and Approximate Simulators
Calibrated NQE enables unbiased field-level cosmological inference from 2D density maps by training mostly on approximate PM simulations and calibrating with ~100 PP simulations.
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