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Large-scale numerical simulations ($\gtrsim 500\rm{Mpc}$) of cosmic reionization are required to match the large survey volume of the upcoming Square Kilometre Array (SKA).
Predicting the output from a complex computer code when fast approximations are available
Kennedy, M. and O’Hagan, A · 2000
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A comparison of three methods for selecting values of input variables in the analysis of output from a computer code
McKay, M. D., Beckman, R. J., and Conover, W. J · 2000
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Freeze the discriminator: a simple baseline for fine-tuning gans, 2020
Mo, S., Cho, M., and Shin, J · 2002
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21cmfast: a fast, seminumerical simulation of the high-redshift 21-cm signal
Mesinger, A., Furlanetto, S., and Cen, R · 2010
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Group invariant scattering
Mallat, S · 2012
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Generative Adversarial Networks
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Deep residual learning for image recognition, 2015
He, K., Zhang, X., Ren, S., and Sun, J · 2015
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Simultaneously constraining the astrophysics of reionisation and the epoch of heating with 21cmmc
Greig, B. and Mesinger, A · 2017
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Progressive Growing of GANs for Improved Quality, Stability, and Variation
Karras, T., Aila, T., Laine, S., and Lehtinen, J · 2017
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The RWST, a comprehensive statistical description of the non-Gaussian structures in the ISM
Allys, E., Levrier, F., Zhang, S., Colling, C., Regaldo-Saint Blancard, B., Boulanger, F., Hennebelle, P., and Mallat, S · 2019
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Fast likelihood-free cosmology with neural density estimators and active learning
Alsing, J., Charnock, T., Feeney, S., and Wandelt, B · 2019
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Painting with baryons: augmenting N-body simulations with gas using deep generative models
Tröster, T., Ferguson, C., Harnois-Déraps, J., and McCarthy, I. G · 2019
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Kymatio: Scattering transforms in python
Andreux, M., Angles, T., Exarchakis, G., Leonarduzzi, R., Rochette, G., Thiry, L., Zarka, J., Mallat, S., Andén, J., Belilovsky, E., Bruna, J., Lostanlen, V., Chaudhary, M., Hirn, M. J., Oyallon, E., Zhang, S., Cella, C., and Eickenberg, M · 2020
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A new approach to observational cosmology using the scattering transform
Cheng, S., Ting, Y.-S., Mé nard, B., and Bruna, J · 2020
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Multifidelity emulation for the matter power spectrum using gaussian processes
Ho, M.-F., Bird, S., and Shelton, C. R · 2021
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Introducing the THESAN
Kannan, R., Garaldi, E., Smith, A., Pakmor, R., Springel, V., Vogelsberger, M., and Hernquist, L · 2021
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Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis
Liu, B., Zhu, Y., Song, K., and Elgammal, A · 2021
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Few-shot Image Generation via Cross-domain Correspondence
Ojha, U., Li, Y., Lu, J., Efros, A. A., Lee, Y. J., Shechtman, E., and Zhang, R · 2021
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Emulating cosmological multifields with generative adversarial networks
Andrianomena, S., Villaescusa-Navarro, F., and Hassan, S · 2022
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Analyzing and improving the image quality of StyleGAN
Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., and Aila, T · 2020
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A unified framework for 21 cm tomography sample generation and parameter inference with progressively growing GANs
List, F. and Lewis, G. F · 2020
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21cmfast v3: A python-integrated c code for generating 3d realizations of the cosmic 21cm signal
Murray, S. G., Greig, B., Mesinger, A., Muñoz, J. B., Qin, Y., Park, J., and Watkinson, C. A · 2020
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How to quantify fields or textures? A guide to the scattering transform
Cheng, S. and Ménard, B · 2021
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Greig, B., Ting, Y.-S., and Kaurov, A. A · 2022
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HIFlow: Generating diverse hi maps and inferring cosmology while marginalizing over astrophysics using normalizing flows
Hassan, S., Villaescusa-Navarro, F., Wandelt, B., Spergel, D. N., Anglé s-Alcázar, D., Genel, S., Cranmer, M., Bryan, G. L., Davé, R., Somerville, R. S., Eickenberg, M., Narayanan, D., Ho, S., and Andrianomena, S · 2022
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A tomographic spherical mass map emulator of the KiDS-1000 survey using conditional generative adversarial networks
Yiu, T. W. H., Fluri, J., and Kacprzak, T · 2022
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Implicit likelihood inference of reionization parameters from the 21 cm power spectrum
Zhao, X., Mao, Y., and Wandelt, B. D · 2022
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