3D-Spatiotemporal Forecasting the Expansion of Supernova Shells Using Deep Learning toward High-Resolution Galaxy Simulations

• URL: http://arxiv.org/abs/2302.00026v3
• Date: Mon, 18 Sep 2023 00:56:15 GMT
• Title: 3D-Spatiotemporal Forecasting the Expansion of Supernova Shells Using Deep Learning toward High-Resolution Galaxy Simulations
• Authors: Keiya Hirashima, Kana Moriwaki, Michiko S. Fujii, Yutaka Hirai, Takayuki R. Saitoh, Junichiro Makino
• Abstract summary: Short integration timesteps for Supernova (SNe) are serious bottlenecks in high-resolution galaxy simulations. We develop a deep learning model, 3D-MIM, to predict a shell expansion after a SN explosion.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Supernova (SN) plays an important role in galaxy formation and evolution. In high-resolution galaxy simulations using massively parallel computing, short integration timesteps for SNe are serious bottlenecks. This is an urgent issue that needs to be resolved for future higher-resolution galaxy simulations. One possible solution would be to use the Hamiltonian splitting method, in which regions requiring short timesteps are integrated separately from the entire system. To apply this method to the particles affected by SNe in a smoothed-particle hydrodynamics simulation, we need to detect the shape of the shell on and within which such SN-affected particles reside during the subsequent global step in advance. In this paper, we develop a deep learning model, 3D-MIM, to predict a shell expansion after a SN explosion. Trained on turbulent cloud simulations with particle mass $m_{\rm gas}=1$M$_\odot$, the model accurately reproduces the anisotropic shell shape, where densities decrease by over 10 per cent by the explosion. We also demonstrate that the model properly predicts the shell radius in the uniform medium beyond the training dataset of inhomogeneous turbulent clouds. We conclude that our model enables the forecast of the shell and its interior where SN-affected particles will be present.

Related papers

• Unfolding Time: Generative Modeling for Turbulent Flows in 4D [49.843505326598596]
  This work introduces a 4D generative diffusion model and a physics-informed guidance technique that enables the generation of realistic sequences of flow states. Our findings indicate that the proposed method can successfully sample entire subsequences from the turbulent manifold. This advancement opens doors for the application of generative modeling in analyzing the temporal evolution of turbulent flows.
  arXiv  Detail & Related papers  (2024-06-17T10:21:01Z)
• Surrogate Modeling for Computationally Expensive Simulations of Supernovae in High-Resolution Galaxy Simulations [0.7927502566022343]
  We develop a method combining machine learning and Gibbs sampling to predict how a supernova affects the surrounding gas. Our method can replace the SN sub-grid models and help properly simulate un-resolved SN feedback in galaxy formation simulations.
  arXiv  Detail & Related papers  (2023-11-14T19:00:03Z)
• NeuralClothSim: Neural Deformation Fields Meet the Thin Shell Theory [70.10550467873499]
  We propose NeuralClothSim, a new quasistatic cloth simulator using thin shells. Our memory-efficient solver operates on a new continuous coordinate-based surface representation called neural deformation fields. NDFs are adaptive: They allocate their capacity to the deformation details and 2) allow surface state queries at arbitrary spatial resolutions without re-training.
  arXiv  Detail & Related papers  (2023-08-24T17:59:54Z)
• Towards Complex Dynamic Physics System Simulation with Graph Neural ODEs [75.7104463046767]
  This paper proposes a novel learning based simulation model that characterizes the varying spatial and temporal dependencies in particle systems. We empirically evaluate GNSTODE's simulation performance on two real-world particle systems, Gravity and Coulomb.
  arXiv  Detail & Related papers  (2023-05-21T03:51:03Z)
• Particle clustering in turbulence: Prediction of spatial and statistical properties with deep learning [6.91821181311687]
  We simulate the dynamics of particles in the Epstein drag regime within a periodic domain of isotropic forced hydrodynamic turbulence. We train a U-Net deep learning model to predict gridded representations of the particle density and velocity fields, given as input the corresponding fluid fields. Our results suggest that, given appropriately expanded training data, deep learning could complement direct numerical simulations in predicting particle clustering within turbulent flows.
  arXiv  Detail & Related papers  (2022-10-05T15:46:28Z)
• Learning Large-scale Subsurface Simulations with a Hybrid Graph Network Simulator [57.57321628587564]
  We introduce Hybrid Graph Network Simulator (HGNS) for learning reservoir simulations of 3D subsurface fluid flows. HGNS consists of a subsurface graph neural network (SGNN) to model the evolution of fluid flows, and a 3D-U-Net to model the evolution of pressure. Using an industry-standard subsurface flow dataset (SPE-10) with 1.1 million cells, we demonstrate that HGNS is able to reduce the inference time up to 18 times compared to standard subsurface simulators.
  arXiv  Detail & Related papers  (2022-06-15T17:29:57Z)
• Entanglement and correlations in fast collective neutrino flavor oscillations [68.8204255655161]
  Collective neutrino oscillations play a crucial role in transporting lepton flavor in astrophysical settings. We study the full out-of-equilibrium flavor dynamics in simple multi-angle geometries displaying fast oscillations. We present evidence that these fast collective modes are generated by the same dynamical phase transition.
  arXiv  Detail & Related papers  (2022-03-05T17:00:06Z)
• Visualizing spinon Fermi surfaces with time-dependent spectroscopy [62.997667081978825]
  We propose applying time-dependent photo-emission spectroscopy, an established tool in solid state systems, in cold atom quantum simulators. We show in exact diagonalization simulations of the one-dimensional $t-J$ model that the spinons start to populate previously unoccupied states in an effective band structure. The dependence of the spectral function on the time after the pump pulse reveals collective interactions among spinons.
  arXiv  Detail & Related papers  (2021-05-27T18:00:02Z)
• Learning the Evolution of the Universe in N-body Simulations [27.935462625522575]
  Large N-body simulations have been built to obtain predictions in the non-linear regime. N-body simulations are computationally expensive and generate large amount of data, putting burdens on storage. We employ a deep neural network model to predict the nonlinear N-body simulation at an intermediate time step.
  arXiv  Detail & Related papers  (2020-12-10T06:27:12Z)
• Fast and Accurate Non-Linear Predictions of Universes with Deep Learning [21.218297581239664]
  We build a V-Net based model that transforms fast linear predictions into fully nonlinear predictions from numerical simulations. Our NN model learns to emulate the simulations down to small scales and is both faster and more accurate than the current state-of-the-art approximate methods.
  arXiv  Detail & Related papers  (2020-12-01T03:30:37Z)
• Predicting Localized Primordial Star Formation with Deep Convolutional Neural Networks [0.0]
  We investigate applying 3D deep convolutional neural networks as fast surrogate models of the formation and feedback effects of primordial stars. We present the surrogate model to predict localized primordial star formation; the feedback model will be presented in a subsequent paper. To our knowledge, this is the first model that can predict primordial star forming regions that match highly-resolved cosmological simulations.
  arXiv  Detail & Related papers  (2020-11-02T22:32:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.