Learning to predict the cosmological structure formation

By Siyu He, Yin Li, Yu Feng, Shirley Ho, Siamak Ravanbakhsh, Barnab'as P'oczos. 2018

192 citations recorded by the source corpus.

Significance To understand the evolution of the Universe requires a concerted effort of accurate observation of the sky and fast prediction of structures in the Universe. N-body simulation is an effective approach to predicting structure formation of the Universe, though computationally expensive. Here, we build a deep neural network to predict structure formation of the Universe. It outperforms the traditional fast-analytical approximation and accurately extrapolates far beyond its training data. Our study proves that deep learning is an accurate alternative to the traditional way of generating approximate cosmological simulations. Our study shows that one can use deep learning to generate complex 3D simulations in cosmology. This suggests that deep learning can provide a powerful alternative to traditional numerical simulations in cosmology. 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 (D3M), 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 D3M 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 D3M 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.

Subfields: Astronomy and Astrophysics, Instrumentation, AI for Physical Sciences

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Citations in the corpus

Toward Causal Representation Learning (2021, background): “One interesting aspect is physics simulation with neural networks [94], which can substantially increase the efﬁciency of hand-engineered simulators [104], [143], [211], [265], [269].”
Learning to Simulate Complex Physics with Graph Networks (2020, background): “…mass-spring, n-body, and robotic control systems (Battaglia et al., 2016; Chang et al., 2016; Sanchez-Gonzalez et al., 2018; Mrowca et al., 2018; Li et al., 2019; Sanchez-Gonzalez et al., 2019), as well as non-physical systems, such as multi-agent dynamics (Tacchetti et al., 2018; Sun et al.,…”
The Quijote Simulations (2019, background): “He et al. (2019) showed that the highly nonlinear structure formation process, simulated with particle-mesh (PM) gravity solver (Feng et al. 2016) with fixed cosmological parameters, can be emulated with convolutional neural networks (CNNs).”
Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning (2019, methodology): “ML models currently outperform state-of-the-art techniques at some of the most sophisticated domain problems across the Natural Sciences (e.g., AlQuraishi, 2019; He et al., 2019; Liu et al., 2016; Mayr et al., 2016).”
Artificial intelligence and illusions of understanding in scientific research (2024, unknown): “In the physical and biological sciences, proposed applications for generative AI include augmenting small datasets or simulating new data to study phenomena such as cosmological structures 42 , medical images 43,44 and nucleic-acid sequences 45 .”