Learning to predict the cosmological structure formation

He, Siyu; Li, Yin; Feng, Yu; Ho, Shirley; Ravanbakhsh, Siamak; Chen, Wei; Póczos, Barnabás

doi:10.1073/pnas.1821458116

Title: Learning to predict the cosmological structure formation

Journal Article · 23 June 2019 · Proceedings of the National Academy of Sciences of the United States of America

DOI: https://doi.org/10.1073/pnas.1821458116 · OSTI ID:1561927

^[1]; Li, Yin ^[2]; Feng, Yu ^[3]; Ho, Shirley ^[4]; Ravanbakhsh, Siamak ^[5]; Chen, Wei ^[6]; Póczos, Barnabás ^[7]

Carnegie Mellon Univ., Pittsburgh, PA (United States); Flatiron Inst. New York, NY (United States); Univ. of Tokyo (Japan)
Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Univ. of Tokyo (Japan)
Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Carnegie Mellon Univ., Pittsburgh, PA (United States); Flatiron Inst. New York, NY (United States); Univ. of Tokyo (Japan); Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Univ. of British Columbia, Vancouver, BC (Canada)
Flatiron Inst. New York, NY (United States)
Carnegie Mellon Univ., Pittsburgh, PA (United States)

Matter evolved under the influence of gravity from minuscule density fluctuations. Nonperturbative structure formed hierarchically over all scales and developed non-Gaussian features in the Universe, known as the cosmic web. To fully understand the structure formation of the Universe is one of the holy grails of modern astrophysics. Astrophysicists survey large volumes of the Universe and use a large ensemble of computer simulations to compare with the observed data to extract the full information of our own Universe. However, to evolve billions of particles over billions of years, even with the simplest physics, is a daunting task. We build a deep neural network, the Deep Density Displacement Model ( $D^{3} M$ ), which learns from a set of prerun numerical simulations, to predict the nonlinear large-scale structure of the Universe with the Zel’dovich Approximation (ZA), an analytical approximation based on perturbation theory, as the input. Our extensive analysis demonstrates that $D^{3} M$ outperforms the second-order perturbation theory (2LPT), the commonly used fast-approximate simulation method, in predicting cosmic structure in the nonlinear regime. We also show that $D^{3} M$ is able to accurately extrapolate far beyond its training data and predict structure formation for significantly different cosmological parameters. Our study proves that deep learning is a practical and accurate alternative to approximate 3D simulations of the gravitational structure formation of the Universe.

View Accepted Manuscript (DOE)

Cite

Export

Save

Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC); National Aeronautics and Space Administration (NASA); Simons Foundation

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1561927

Journal Information:: Proceedings of the National Academy of Sciences of the United States of America, Journal Name: Proceedings of the National Academy of Sciences of the United States of America Journal Issue: 28 Vol. 116; ISSN 0027-8424

Publisher:: National Academy of Sciences, Washington, DC (United States)Copyright Statement

Country of Publication:: United States

Language:: English

References (85)

Lagrangian theory of gravitational instability of Friedman-Lematre cosmologies - a generic third-order model for non-linear clustering Buchert, T. Monthly Notices of the Royal Astronomical Society, Vol. 267, Issue 4 https://doi.org/10.1093/mnras/267.4.811	journal	April 1994
Physics 101: Learning Physical Object Properties from Unlabeled Videos Wu, Jiajun; Lim, Joseph; Zhang, Hongyi Procedings of the British Machine Vision Conference 2016 https://doi.org/10.5244/C.30.39	conference	January 2016
Densely Connected Convolutional Networks Huang, Gao; Liu, Zhuang; van der Maaten, Laurens arXiv https://doi.org/10.48550/arxiv.1608.06993	preprint	January 2016
Photometric Supernova Classification With Machine Learning Lochner, Michelle; McEwen, Jason D.; Peiris, Hiranya V. arXiv https://doi.org/10.48550/arxiv.1603.00882	text	January 2016
The Sdss-Iv Extended Baryon Oscillation Spectroscopic Survey: Overview and Early data Dawson, Kyle S.; Kneib, Jean-Paul; Percival, Will J. The Astronomical Journal, Vol. 151, Issue 2 https://doi.org/10.3847/0004-6256/151/2/44	journal	February 2016
Cosmology and Fundamental Physics with the Euclid Satellite Amendola, Luca; Appleby, Stephen; Bacon, David Living Reviews in Relativity, Vol. 16, Issue 1 https://doi.org/10.12942/lrr-2013-6	journal	September 2013
Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning Alipanahi, Babak; Delong, Andrew; Weirauch, Matthew T. Nature Biotechnology, Vol. 33, Issue 8 https://doi.org/10.1038/nbt.3300	journal	July 2015
Exploring the posterior surface of the large scale structure reconstruction Feng, Yu; Seljak, Uroš; Zaldarriaga, Matias Journal of Cosmology and Astroparticle Physics, Vol. 2018, Issue 07 https://doi.org/10.1088/1475-7516/2018/07/043	journal	July 2018
Mastering the game of Go with deep neural networks and tree search Silver, David; Huang, Aja; Maddison, Chris J. Nature, Vol. 529, Issue 7587 https://doi.org/10.1038/nature16961	journal	January 2016
A New Parallel P3M Code for Very Large-Scale Cosmological Simulations MacFarland, Tom; Couchman, H. M. P.; Pearce, F. R. arXiv https://doi.org/10.48550/arxiv.astro-ph/9805096	text	January 1998
TreePM: A code for cosmological N-body simulations Bagla, J. S. Journal of Astrophysics and Astronomy, Vol. 23, Issue 3-4 https://doi.org/10.1007/BF02702282	journal	December 2002
The Zel'dovich approximation White, Martin Monthly Notices of the Royal Astronomical Society, Vol. 439, Issue 4 https://doi.org/10.1093/mnras/stu209	journal	February 2014
U-Net: Convolutional Networks for Biomedical Image Segmentation Ronneberger, Olaf; Fischer, Philipp; Brox, Thomas Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III https://doi.org/10.1007/978-3-319-24574-4_28	book	November 2015
The Zeldovich approximation White, Martin arXiv https://doi.org/10.48550/arxiv.1401.5466	text	January 2014
Learning Physical Intuition of Block Towers by Example Lerer, Adam; Gross, Sam; Fergus, Rob arXiv https://doi.org/10.48550/arxiv.1603.01312	preprint	January 2016
Deep learning for regulatory genomics Park, Yongjin; Kellis, Manolis Nature Biotechnology, Vol. 33, Issue 8 https://doi.org/10.1038/nbt.3313	journal	August 2015
Lagrangian theory of gravitational instability of Friedman-Lemaitre cosmologies - generic third-order model for non-linear clustering Buchert, Thomas arXiv https://doi.org/10.48550/arxiv.astro-ph/9309055	text	January 1993
Ready or Not, Here We Go Dyster, Timothy; Sheth, Sameer A.; McKhann, Guy M. Neurosurgery, Vol. 78, Issue 6 https://doi.org/10.1227/01.neu.0000484053.82181.f6	journal	June 2016
Long-term Recurrent Convolutional Networks for Visual Recognition and Description Donahue, Jeff; Hendricks, Lisa Anne; Rohrbach, Marcus arXiv https://doi.org/10.48550/arxiv.1411.4389	preprint	January 2014
Human-level control through deep reinforcement learning Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David Nature, Vol. 518, Issue 7540 https://doi.org/10.1038/nature14236	journal	February 2015
Cosmology and fundamental physics with the Euclid satellite Amendola, Luca; Appleby, Stephen; Bacon, David arXiv https://doi.org/10.48550/arxiv.1206.1225	text	January 2012
Cosmology and Fundamental Physics with the Euclid Satellite Amendola, Luca; Appleby, Stephen; Bacon, David Living Reviews in Relativity, Vol. 16, Issue 1 https://doi.org/10.12942/lrr-2013-6	journal	September 2013
Simulation as an engine of physical scene understanding Battaglia, P. W.; Hamrick, J. B.; Tenenbaum, J. B. Proceedings of the National Academy of Sciences, Vol. 110, Issue 45 https://doi.org/10.1073/pnas.1306572110	journal	October 2013
Cosmology and fundamental physics with the Euclid satellite Amendola, Luca; Appleby, Stephen; Avgoustidis, Anastasios ETH Zurich https://doi.org/10.3929/ethz-b-000260964	text	January 2018
Planck 2015 results : XXIII. The thermal Sunyaev-Zeldovich effect-cosmic infrared background correlation Ade, P. A. R.; Aghanim, N.; Arnaud, M. Astronomy & Astrophysics, Vol. 594 https://doi.org/10.1051/0004-6361/201527418	journal	September 2016
Planck 2015 results. XIII. Cosmological parameters Collaboration, Planck; Ade, P. A. R.; Aghanim, N. arXiv https://doi.org/10.48550/arxiv.1502.01589	text	January 2015
FastPM: a new scheme for fast simulations of dark matter and haloes Feng, Yu; Chu, Man-Yat; Seljak, Uroš Monthly Notices of the Royal Astronomical Society, Vol. 463, Issue 3 https://doi.org/10.1093/mnras/stw2123	journal	August 2016
Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning Alipanahi, Babak; Delong, Andrew; Weirauch, Matthew T. Nature Biotechnology, Vol. 33, Issue 8 https://doi.org/10.1038/nbt.3300	journal	July 2015
Planck 2015 results : XIII. Cosmological parameters Ade, P. A. R.; Aghanim, N.; Arnaud, M. Astronomy & Astrophysics, Vol. 594 https://doi.org/10.1051/0004-6361/201525830	journal	September 2016
Planning chemical syntheses with deep neural networks and symbolic AI Segler, Marwin H. S.; Preuss, Mike; Waller, Mark P. Nature, Vol. 555, Issue 7698 https://doi.org/10.1038/nature25978	journal	March 2018
Planning chemical syntheses with deep neural networks and symbolic AI Segler, Marwin H. S.; Preuss, Mike; Waller, Mark P. Nature, Vol. 555, Issue 7698 https://doi.org/10.1038/nature25978	journal	March 2018
Long-term recurrent convolutional networks for visual recognition and description Donahue, Jeff; Hendricks, Lisa Anne; Guadarrama, Sergio 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) https://doi.org/10.1109/CVPR.2015.7298878	conference	June 2015
AI designs organic syntheses Lowe, Derek Nature, Vol. 555, Issue 7698 https://doi.org/10.1038/d41586-018-03774-5	journal	March 2018
Simulation as an engine of physical scene understanding Battaglia, P. W.; Hamrick, J. B.; Tenenbaum, J. B. Proceedings of the National Academy of Sciences, Vol. 110, Issue 45 https://doi.org/10.1073/pnas.1306572110	journal	October 2013
Exploring the posterior surface of the large scale structure reconstruction Feng, Yu; Seljak, Uroš; Zaldarriaga, Matias Journal of Cosmology and Astroparticle Physics, Vol. 2018, Issue 07 https://doi.org/10.1088/1475-7516/2018/07/043	journal	July 2018
Learning Visual Predictive Models of Physics for Playing Billiards Fragkiadaki, Katerina; Agrawal, Pulkit; Levine, Sergey arXiv https://doi.org/10.48550/arxiv.1511.07404	preprint	January 2015
Bayesian physical reconstruction of initial conditions from large-scale structure surveys Jasche, Jens; Wandelt, Benjamin D. Monthly Notices of the Royal Astronomical Society, Vol. 432, Issue 2 https://doi.org/10.1093/mnras/stt449	journal	April 2013
The Baryon Oscillation Spectroscopic Survey of Sdss-Iii Dawson, Kyle S.; Schlegel, David J.; Ahn, Christopher P. The Astronomical Journal, Vol. 145, Issue 1 https://doi.org/10.1088/0004-6256/145/1/10	journal	December 2012
Planck 2015 results : XXVI. The Second Ade, P. A. R.; Aghanim, N.; Argüeso, F. Astronomy & Astrophysics, Vol. 594 https://doi.org/10.1051/0004-6361/201526914	journal	September 2016
Galaxy And Mass Assembly (GAMA): end of survey report and data release 2 Liske, J.; Baldry, I. K.; Driver, S. P. Monthly Notices of the Royal Astronomical Society, Vol. 452, Issue 2 https://doi.org/10.1093/mnras/stv1436	journal	July 2015
A volumetric deep Convolutional Neural Network for simulation of mock dark matter halo catalogues Berger, Philippe; Stein, George Monthly Notices of the Royal Astronomical Society, Vol. 482, Issue 3 https://doi.org/10.1093/mnras/sty2949	journal	November 2018
DeepSphere: Efficient spherical convolutional neural network with HEALPix sampling for cosmological applications Perraudin, N.; Defferrard, M.; Kacprzak, T. Astronomy and Computing, Vol. 27 https://doi.org/10.1016/j.ascom.2019.03.004	journal	April 2019
Long-Term Recurrent Convolutional Networks for Visual Recognition and Description Donahue, Jeff; Hendricks, Lisa Anne; Rohrbach, Marcus IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 39, Issue 4 https://doi.org/10.1109/tpami.2016.2599174	journal	April 2017
Accelerating Eulerian Fluid Simulation With Convolutional Networks Tompson, Jonathan; Schlachter, Kristofer; Sprechmann, Pablo arXiv https://doi.org/10.48550/arxiv.1607.03597	preprint	January 2016
The Baryon Oscillation Spectroscopic Survey of SDSS-III Dawson, Kyle S.; Schlegel, David J.; Ahn, Christopher P. arXiv https://doi.org/10.48550/arxiv.1208.0022	text	January 2012
Deep learning LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey Nature, Vol. 521, Issue 7553 https://doi.org/10.1038/nature14539	journal	May 2015
A new parallel code for very large-scale cosmological simulations MacFarland, Tom; Couchman, H. M. P.; Pearce, F. R. New Astronomy, Vol. 3, Issue 8 https://doi.org/10.1016/S1384-1076(98)00033-5	journal	December 1998
CMU DeepLens: deep learning for automatic image-based galaxy–galaxy strong lens finding Lanusse, François; Ma, Quanbin; Li, Nan Monthly Notices of the Royal Astronomical Society, Vol. 473, Issue 3 https://doi.org/10.1093/mnras/stx1665	journal	July 2017
CosmoFlow: Using Deep Learning to Learn the Universe at Scale Mathuriya, Amrita; Bard, Deborah; Mendygral, Peter SC18: International Conference for High Performance Computing, Networking, Storage and Analysis https://doi.org/10.1109/SC.2018.00068	conference	November 2018
The 2dF Galaxy Redshift Survey: spectra and redshifts Colless, Matthew; Dalton, Gavin; Maddox, Steve Monthly Notices of the Royal Astronomical Society, Vol. 328, Issue 4 https://doi.org/10.1046/j.1365-8711.2001.04902.x	journal	December 2001
Sdss-Iii: Massive Spectroscopic Surveys of the Distant Universe, the Milky way, and Extra-Solar Planetary Systems Eisenstein, Daniel J.; Weinberg, David H.; Agol, Eric The Astronomical Journal, Vol. 142, Issue 3 https://doi.org/10.1088/0004-6256/142/3/72	journal	August 2011
Planck 2015 results : XVI. Isotropy and statistics of the CMB Ade, P. A. R.; Aghanim, N.; Akrami, Y. Astronomy & Astrophysics, Vol. 594 https://doi.org/10.1051/0004-6361/201526681	journal	September 2016
Bayesian physical reconstruction of initial conditions from large-scale structure surveys Jasche, Jens; Wandelt, Benjamin D. Monthly Notices of the Royal Astronomical Society, Vol. 432, Issue 2 https://doi.org/10.1093/mnras/stt449	journal	April 2013
Cosmology and fundamental physics with the Euclid satellite Amendola, Luca; Appleby, Stephen; Avgoustidis, Anastasios Living Reviews in Relativity, Vol. 21, Issue 1 https://doi.org/10.1007/s41114-017-0010-3	journal	April 2018
GADGET: a code for collisionless and gasdynamical cosmological simulations Springel, Volker; Yoshida, Naoki; White, Simon D. M. New Astronomy, Vol. 6, Issue 2 https://doi.org/10.1016/S1384-1076(01)00042-2	journal	April 2001
Fast automated analysis of strong gravitational lenses with convolutional neural networks Hezaveh, Yashar D.; Levasseur, Laurence Perreault; Marshall, Philip J. Nature, Vol. 548, Issue 7669 https://doi.org/10.1038/nature23463	journal	August 2017
Planck 2015 results : X. Diffuse component separation: Foreground maps Adam, R.; Ade, P. A. R.; Aghanim, N. Astronomy & Astrophysics, Vol. 594 https://doi.org/10.1051/0004-6361/201525967	journal	September 2016
How filaments of galaxies are woven into the cosmic web Bond, J. Richard; Kofman, Lev; Pogosyan, Dmitry Nature, Vol. 380, Issue 6575 https://doi.org/10.1038/380603a0	journal	April 1996
The 6dF Galaxy Survey: final redshift release (DR3) and southern large-scale structures Jones, D. Heath; Read, Mike A.; Saunders, Will Monthly Notices of the Royal Astronomical Society, Vol. 399, Issue 2 https://doi.org/10.1111/j.1365-2966.2009.15338.x	journal	October 2009
The initial conditions of the Universe from constrained simulations Kitaura, Francisco-Shu Monthly Notices of the Royal Astronomical Society: Letters, Vol. 429, Issue 1 https://doi.org/10.1093/mnrasl/sls029	journal	November 2012
Exploring the posterior surface of the large scale structure reconstruction Feng, Yu; Seljak, Uros; Zaldarriaga, Matias arXiv https://doi.org/10.48550/arxiv.1804.09687	text	January 2018
Opportunities and obstacles for deep learning in biology and medicine Ching, Travers; Himmelstein, Daniel S.; Beaulieu-Jones, Brett K. Journal of The Royal Society Interface, Vol. 15, Issue 141 https://doi.org/10.1098/rsif.2017.0387	journal	April 2018
A volumetric deep Convolutional Neural Network for simulation of mock dark matter halo catalogues Berger, Philippe; Stein, George arXiv https://doi.org/10.48550/arxiv.1805.04537	text	January 2018
Machine learning for quantum physics Hush, Michael R. Science, Vol. 355, Issue 6325 https://doi.org/10.1126/science.aam6564	journal	February 2017
Opportunities and obstacles for deep learning in biology and medicine Ching, Travers; Himmelstein, Daniel S.; Beaulieu-Jones, Brett K. The Royal Society Interface https://doi.org/10.1101/142760	posted_content	January 2018
The SDSS-IV extended Baryon Oscillation Spectroscopic Survey: Overview and Early Data Dawson, Kyle S.; Kneib, Jean-Paul; Percival, Will J. arXiv https://doi.org/10.48550/arxiv.1508.04473	text	January 2015
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation Milletari, Fausto; Navab, Nassir; Ahmadi, Seyed-Ahmad arXiv https://doi.org/10.48550/arxiv.1606.04797	preprint	January 2016
Fast automated analysis of strong gravitational lenses with convolutional neural networks Hezaveh, Yashar D.; Levasseur, Laurence Perreault; Marshall, Philip J. Nature, Vol. 548, Issue 7669 https://doi.org/10.1038/nature23463	journal	August 2017
Deep learning LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey Nature, Vol. 521, Issue 7553 https://doi.org/10.1038/nature14539	journal	May 2015
Deep Learning Ganatra, Sanket Zenodo https://doi.org/10.5281/zenodo.1189359	text	January 2018
Helmholtz decomposition of the Lagrangian displacement Chan, Kwan Chuen Physical Review D, Vol. 89, Issue 8 https://doi.org/10.1103/PhysRevD.89.083515	journal	April 2014
Photometric Supernova Classification with Machine Learning Lochner, Michelle; McEwen, Jason D.; Peiris, Hiranya V. The Astrophysical Journal Supplement Series, Vol. 225, Issue 2 https://doi.org/10.3847/0067-0049/225/2/31	journal	August 2016
nbodykit: An Open-source, Massively Parallel Toolkit for Large-scale Structure Hand, Nick; Feng, Yu; Beutler, Florian The Astronomical Journal, Vol. 156, Issue 4 https://doi.org/10.3847/1538-3881/aadae0	journal	September 2018
Solving the quantum many-body problem with artificial neural networks Carleo, Giuseppe; Troyer, Matthias Science, Vol. 355, Issue 6325 https://doi.org/10.1126/science.aag2302	journal	February 2017
Computing the three-point correlation function of galaxies in $\mathcal {O}(N^2)$ time Slepian, Zachary; Eisenstein, Daniel J. Monthly Notices of the Royal Astronomical Society, Vol. 454, Issue 4 https://doi.org/10.1093/mnras/stv2119	journal	October 2015
Star–galaxy classification using deep convolutional neural networks Kim, Edward J.; Brunner, Robert J. Monthly Notices of the Royal Astronomical Society, Vol. 464, Issue 4 https://doi.org/10.1093/mnras/stw2672	journal	October 2016
Galaxy And Mass Assembly (GAMA): end of survey report and data release 2 Liske, J.; Baldry, I. K.; Driver, S. P. Monthly Notices of the Royal Astronomical Society, Vol. 452, Issue 2 https://doi.org/10.1093/mnras/stv1436	journal	July 2015
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation Milletari, Fausto; Navab, Nassir; Ahmadi, Seyed-Ahmad 2016 Fourth International Conference on 3D Vision (3DV) https://doi.org/10.1109/3DV.2016.79	conference	October 2016
The evolution of large-scale structure in a universe dominated by cold dark matter Davis, M.; Efstathiou, G.; Frenk, C. S. The Astrophysical Journal, Vol. 292 https://doi.org/10.1086/163168	journal	May 1985
Densely Connected Convolutional Networks Huang, Gao; Liu, Zhuang; Maaten, Laurens van der 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) https://doi.org/10.1109/CVPR.2017.243	conference	July 2017
Computing the Three-Point Correlation Function of Galaxies in $\mathcal{O}(N^2)$ Time Slepian, Zachary; Eisenstein, Daniel J. arXiv https://doi.org/10.48550/arxiv.1506.02040	text	January 2015
DeepSphere: Efficient spherical convolutional neural network with HEALPix sampling for cosmological applications Perraudin, N.; Defferrard, M.; Kacprzak, T. Astronomy and Computing, Vol. 27 https://doi.org/10.1016/j.ascom.2019.03.004	journal	April 2019
DeepCMB: Lensing reconstruction of the cosmic microwave background with deep neural networks Caldeira, J.; Wu, W. L. K.; Nord, B. Astronomy and Computing, Vol. 28 https://doi.org/10.1016/j.ascom.2019.100307	journal	July 2019
Newtonian Image Understanding: Unfolding the Dynamics of Objects in Static Images Mottaghi, Roozbeh; Bagherinezhad, Hessam; Rastegari, Mohammad 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) https://doi.org/10.1109/CVPR.2016.383	conference	June 2016
TreePM: A code for cosmological N-body simulations Bagla, J. S. Journal of Astrophysics and Astronomy, Vol. 23, Issue 3-4 https://doi.org/10.1007/bf02702282	journal	December 2002

Cited By (10)

Learning effective physical laws for generating cosmological hydrodynamics with Lagrangian Deep Learning Dai, Biwei; Seljak, Uros arXiv https://doi.org/10.48550/arxiv.2010.02926	text	January 2020
A Hybrid Deep Learning Approach to Cosmological Constraints From Galaxy Redshift Surveys Ntampaka, Michelle; Eisenstein, Daniel J.; Yuan, Sihan arXiv https://doi.org/10.48550/arxiv.1909.10527	text	January 2019
Learning to Simulate Complex Physics with Graph Networks Sanchez-Gonzalez, Alvaro; Godwin, Jonathan; Pfaff, Tobias arXiv https://doi.org/10.48550/arxiv.2002.09405	preprint	January 2020
Fast and Accurate Non-Linear Predictions of Universes with Deep Learning de Oliveira, Renan Alves; Li, Yin; Villaescusa-Navarro, Francisco arXiv https://doi.org/10.48550/arxiv.2012.00240	preprint	January 2020
Painting halos from cosmic density fields of dark matter with physically motivated neural networks Kodi Ramanah, Doogesh; Charnock, Tom; Lavaux, Guilhem Physical Review D, Vol. 100, Issue 4 https://doi.org/10.1103/physrevd.100.043515	journal	August 2019
Painting halos from cosmic density fields of dark matter with physically motivated neural networks Ramanah, Doogesh Kodi; Charnock, Tom; Lavaux, Guilhem arXiv https://doi.org/10.48550/arxiv.1903.10524	text	January 2019
Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning Kratzert, Frederik; Klotz, Daniel; Herrnegger, Mathew Water Resources Research, Vol. 55, Issue 12 https://doi.org/10.1029/2019wr026065	journal	December 2019
A Hybrid Deep Learning Approach to Cosmological Constraints from Galaxy Redshift Surveys Ntampaka, Michelle; Eisenstein, Daniel J.; Yuan, Sihan The Astrophysical Journal, Vol. 889, Issue 2 https://doi.org/10.3847/1538-4357/ab5f5e	journal	February 2020
Investigating cosmological GAN emulators using latent space interpolation Tamosiunas, Andrius; Winther, Hans A.; Koyama, Kazuya Monthly Notices of the Royal Astronomical Society, Vol. 506, Issue 2 https://doi.org/10.1093/mnras/stab1879	journal	July 2021
Unveiling the predictive power of static structure in glassy systems Bapst, V.; Keck, T.; Grabska-Barwińska, A. Nature Physics, Vol. 16, Issue 4 https://doi.org/10.1038/s41567-020-0842-8	journal	April 2020

Similar Records

Non-analyticity in holographic complexity near critical points

Journal Article · 2023 · Journal of Physics. A, Mathematical and Theoretical · OSTI ID:2419759

Sood, Uday; Kruczenski, Martin

Structure of pressure-gradient-driven current singularity in ideal magnetohydrodynamic equilibrium

Journal Article · 2023 · Plasma Physics and Controlled Fusion · OSTI ID:1926781

Huang, Yi-Min; Zhou, Yao; Loizu, Joaquim; +2 more

Scalar fields in 3D asymptotically flat higher-spin gravity

Journal Article · 2021 · Journal of Physics. A, Mathematical and Theoretical · OSTI ID:1851023

Ammon, Martin; Pannier, Michel; Riegler, Max

Related Subjects

79 ASTRONOMY AND ASTROPHYSICS
cosmology
deep learning
simulation

Title: Learning to predict the cosmological structure formation

Citation Formats

References (85)

Cited By (10)

Similar Records

Related Subjects