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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.01576 |
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| _version_ | 1866914176068222976 |
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| author | Bhojwani, Nihaal Wang, Chuwei Wang, Hai-Yang Sun, Chang Most, Elias R. Anandkumar, Anima |
| author_facet | Bhojwani, Nihaal Wang, Chuwei Wang, Hai-Yang Sun, Chang Most, Elias R. Anandkumar, Anima |
| contents | Modeling how supermassive black holes co-evolve with their host galaxies is notoriously hard because the relevant physics spans nine orders of magnitude in scale-from milliparsecs to megaparsecs--making end-to-end first-principles simulation infeasible. To characterize the feedback from the small scales, existing methods employ a static subgrid scheme or one based on theoretical guesses, which usually struggle to capture the time variability and derive physically faithful results. Neural operators are a class of machine learning models that achieve significant speed-up in simulating complex dynamics. We introduce a neural-operator-based ''subgrid black hole'' that learns the small-scale local dynamics and embeds it within the direct multi-level simulations. Trained on small-domain (general relativistic) magnetohydrodynamic data, the model predicts the unresolved dynamics needed to supply boundary conditions and fluxes at coarser levels across timesteps, enabling stable long-horizon rollouts without hand-crafted closures. Thanks to the great speedup in fine-scale evolution, our approach for the first time captures intrinsic variability in accretion-driven feedback, allowing dynamic coupling between the central black hole and galaxy-scale gas. This work reframes subgrid modeling in computational astrophysics with scale separation and provides a scalable path toward data-driven closures for a broad class of systems with central accretors. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_01576 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Black Hole to Galaxy: Neural Operator: Framework for Accretion and Feedback Dynamics Bhojwani, Nihaal Wang, Chuwei Wang, Hai-Yang Sun, Chang Most, Elias R. Anandkumar, Anima High Energy Astrophysical Phenomena Astrophysics of Galaxies Artificial Intelligence General Relativity and Quantum Cosmology Modeling how supermassive black holes co-evolve with their host galaxies is notoriously hard because the relevant physics spans nine orders of magnitude in scale-from milliparsecs to megaparsecs--making end-to-end first-principles simulation infeasible. To characterize the feedback from the small scales, existing methods employ a static subgrid scheme or one based on theoretical guesses, which usually struggle to capture the time variability and derive physically faithful results. Neural operators are a class of machine learning models that achieve significant speed-up in simulating complex dynamics. We introduce a neural-operator-based ''subgrid black hole'' that learns the small-scale local dynamics and embeds it within the direct multi-level simulations. Trained on small-domain (general relativistic) magnetohydrodynamic data, the model predicts the unresolved dynamics needed to supply boundary conditions and fluxes at coarser levels across timesteps, enabling stable long-horizon rollouts without hand-crafted closures. Thanks to the great speedup in fine-scale evolution, our approach for the first time captures intrinsic variability in accretion-driven feedback, allowing dynamic coupling between the central black hole and galaxy-scale gas. This work reframes subgrid modeling in computational astrophysics with scale separation and provides a scalable path toward data-driven closures for a broad class of systems with central accretors. |
| title | From Black Hole to Galaxy: Neural Operator: Framework for Accretion and Feedback Dynamics |
| topic | High Energy Astrophysical Phenomena Astrophysics of Galaxies Artificial Intelligence General Relativity and Quantum Cosmology |
| url | https://arxiv.org/abs/2512.01576 |