Variational autoencoder for generating realistic $N$-body simulations for dark matter halos | Paper Citation Record

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This paper cites The cosmological simulation code gadget-2.Monthly notices of the royal astronomical society, 364(4):1105–1134, 2005.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos The cosmological simulation code gadget-2.Monthly notices of the royal astronomical society, 364(4):1105–1134, 2005

Reference 1

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Observation ffba013c-0e45-4901-9ad6-44a766f2f208 · outbound

This paper cites Eisenstein.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Eisenstein

Reference 2

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This paper cites Simulating cosmic structure formation with the gadget-4 code.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Simulating cosmic structure formation with the gadget-4 code

Reference 3

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This paper cites Enzo: An adaptive mesh refinement code for astrophysics.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Enzo: An adaptive mesh refinement code for astrophysics

Reference 4

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This paper cites Deep learning.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Deep learning

Reference 5

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This paper cites Generative adversarial nets.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Generative adversarial nets

Reference 6

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This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Score-Based Generative Modeling through Stochastic Differential Equations

Reference 7

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This paper cites On the Design Fundamentals of Diffusion Models: A Survey.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos On the Design Fundamentals of Diffusion Models: A Survey

Reference 8

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Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Unresolved cited work

Reference 9

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Observation 709049c6-540b-4b98-aaad-8091508557f5 · outbound

This paper cites Autoencoders, minimum description length and helmholtz free energy.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Autoencoders, minimum description length and helmholtz free energy

Reference 10

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Observation ab4e560f-883c-4c50-bf86-b516b406d264 · outbound

This paper cites Analysis of dark matter halo structure formation in n-body simulations with machine learning.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Analysis of dark matter halo structure formation in n-body simulations with machine learning

Reference 11

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This paper cites A deep-learning model for the density profiles of subhaloes in illustristng.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos A deep-learning model for the density profiles of subhaloes in illustristng

Reference 12

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Observation 8d428f61-75ad-4c93-9b98-e06ff334ddde · outbound

This paper cites Predicting dark matter halo formation in n-body simulations with deep regression networks.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Predicting dark matter halo formation in n-body simulations with deep regression networks

Reference 13

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Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Unresolved cited work

Reference 14

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This paper cites Deep learning and genetic algorithms for cosmological bayesian inference speed-up.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Deep learning and genetic algorithms for cosmological bayesian inference speed-up

Reference 15

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This paper cites A deep learning model to emulate simulations of cosmic reionization.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos A deep learning model to emulate simulations of cosmic reionization

Reference 16

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This paper cites Cosmoflow: Using deep learning to learn the universe at scale.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Cosmoflow: Using deep learning to learn the universe at scale

Reference 17

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This paper cites Linna: Likelihood inference neural network accelerator.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Linna: Likelihood inference neural network accelerator

Reference 18

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This paper cites Cosmological n-body simulations: a challenge for scalable generative models.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Cosmological n-body simulations: a challenge for scalable generative models

Reference 19

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Observation 0ab6b9b1-3d3c-47a7-8c0d-38c2df396b48 · outbound

This paper cites Encoding large-scale cosmological structure with generative adversarial networks.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Encoding large-scale cosmological structure with generative adversarial networks

Reference 20

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Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Unresolved cited work

Reference 21

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Observation fb7dcd9e-86ec-4c5b-98b9-f4cd6a9c0a96 · outbound

This paper cites Superresolution emulation of large cosmological fields with a 3d conditional diffusion model.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Superresolution emulation of large cosmological fields with a 3d conditional diffusion model

Reference 22

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This paper cites Can denoising diffusion probabilistic models generate realistic astrophysical fields?.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Can denoising diffusion probabilistic models generate realistic astrophysical fields?

Reference 23

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This paper cites Cosmological Field Emulation and Parameter Inference with Diffusion Models.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Cosmological Field Emulation and Parameter Inference with Diffusion Models

Reference 24

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This paper cites Stochastic Super-resolution of Cosmological Simulations with Denoising Diffusion Models.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Stochastic Super-resolution of Cosmological Simulations with Denoising Diffusion Models

Reference 25

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Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Unresolved cited work

Reference 26

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Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Unresolved cited work

Reference 27

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Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Unresolved cited work

Reference 28

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This paper cites Lsst science book, version 2.0.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Lsst science book, version 2.0

Reference 29

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Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Unresolved cited work

Reference 30

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Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Unresolved cited work

Reference 31

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This paper cites Neural network reconstructions for the hubble parameter, growth rate and distance modulus.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Neural network reconstructions for the hubble parameter, growth rate and distance modulus

Reference 32

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This paper cites Cosmo vae: Variational autoencoder for cmb image inpainting.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Cosmo vae: Variational autoencoder for cmb image inpainting

Reference 33

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This paper cites Accelerating lensed quasar discovery and modeling with physics-informed variational autoencoders.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Accelerating lensed quasar discovery and modeling with physics-informed variational autoencoders

Reference 34

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This paper cites Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy.Nature Physics, 18(1):112–117, 2022.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy.Nature Physics, 18(1):112–117, 2022

Reference 35

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This paper cites Alberto Vazquez, and Ruslan Gabbasov.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Alberto Vazquez, and Ruslan Gabbasov

Reference 36

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This paper cites A hierarchical O(N log N) force-calculation algorithm.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos A hierarchical O(N log N) force-calculation algorithm

Reference 37

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Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Unresolved cited work

Reference 38

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Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Unresolved cited work

Reference 39

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This paper cites Gewers, Gustavo R.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Gewers, Gustavo R

Reference 40

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This paper cites Kingma and Max Welling.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Kingma and Max Welling

Reference 41

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This paper cites O’Reilly Media, Inc.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos O’Reilly Media, Inc

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Variational autoencoder for generating realistic $N$-body simulations for dark matter halos On information and sufficiency

Reference 43

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This paper cites Understanding Diffusion Models: A Unified Perspective, August 2022.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Understanding Diffusion Models: A Unified Perspective, August 2022

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This paper cites Neural networks optimized by genetic algorithms in cosmology.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Neural networks optimized by genetic algorithms in cosmology

Reference 45

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This paper cites Optuna: A next-generation hyperparameter optimization framework.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Optuna: A next-generation hyperparameter optimization framework

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This paper cites Reconstruction of dark energy and expansion dynamics using gaussian processes.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Reconstruction of dark energy and expansion dynamics using gaussian processes

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This paper cites Gaussian processes reconstruction of dark energy from observational data.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Gaussian processes reconstruction of dark energy from observational data

Reference 48

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This paper cites Escamilla, Purba Mukherjee, and J.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Escamilla, Purba Mukherjee, and J

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Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Unresolved cited work

Reference 50

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This paper cites Revising the halofit model for the nonlinear matter power spectrum.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Revising the halofit model for the nonlinear matter power spectrum

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Observation fe9e1107-bd1f-4805-88f1-7ca071f813a1 · outbound

This paper cites Machine learning unveils the linear matter power spectrum of modified gravity.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Machine learning unveils the linear matter power spectrum of modified gravity

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This paper cites Hunting down systematics in baryon acoustic oscillations after cosmic high noon.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Hunting down systematics in baryon acoustic oscillations after cosmic high noon

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Observation d749e82b-25d6-4404-8fc3-c6fa80858a98 · outbound

This paper cites Measurement of the power spectrum turnover scale from the cross-correlation between CMB lensing and Quaia.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Measurement of the power spectrum turnover scale from the cross-correlation between CMB lensing and Quaia

Reference 54

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Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Unresolved cited work

Reference 55

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Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Mock galaxy catalogues using the quick particle mesh method

Reference 56

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This paper cites On the asymptotic behaviour of cosmic density- fluctuation power spectra of cold dark matter.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos On the asymptotic behaviour of cosmic density- fluctuation power spectra of cold dark matter

Reference 57

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This paper cites Widrow, Pascal J.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Widrow, Pascal J

Reference 58

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This paper cites Gravitational turbulence: the small-scale limit of the cold-dark-matter power spectrum.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Gravitational turbulence: the small-scale limit of the cold-dark-matter power spectrum

Reference 59

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This paper cites Raissi, P.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Raissi, P

Reference 60

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This paper cites Kevrekidis, Lu Lu, Paris Perdikaris, Sifan Wang, and Liu Yang.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Kevrekidis, Lu Lu, Paris Perdikaris, Sifan Wang, and Liu Yang

Reference 61

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Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Unresolved cited work

Reference 62

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This paper cites Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:35.157089Z

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This paper cites Latentpinns: Generative physics-informed neural networks via a latent representation learning.

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Latentpinns: Generative physics-informed neural networks via a latent representation learning

Reference 64

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