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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/2504.14180 |
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| _version_ | 1866917006116126720 |
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| author | Zhang, Xiaoyu Yi, Yuxiao Wang, Lile Xu, Zhi-Qin John Zhang, Tianhan Zhou, Yao |
| author_facet | Zhang, Xiaoyu Yi, Yuxiao Wang, Lile Xu, Zhi-Qin John Zhang, Tianhan Zhou, Yao |
| contents | In astrophysical simulations, nuclear reacting flows pose computational challenges due to the stiffness of reaction networks. We introduce neural network-based surrogate models using the DeePODE framework to enhance simulation efficiency while maintaining accuracy and robustness. Our method replaces conventional stiff ODE solvers with deep learning models trained through evolutionary Monte Carlo sampling from zero-dimensional simulation data, ensuring generalization across varied thermonuclear and hydrodynamic conditions. Tested on 3-species and 13-species reaction networks, the models achieve $\lesssim 1\%$ accuracy relative to semi-implicit numerical solutions and deliver a $\sim 2.6\times$ speedup on CPUs. A temperature-thresholded deployment strategy ensures stability in extreme conditions, sustaining neural network utilization above 75\% in multi-dimensional simulations. These data-driven surrogates effectively mitigate stiffness constraints, offering a scalable approach for high-fidelity modeling of astrophysical nuclear reacting flows. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_14180 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep Neural Networks for Modeling Astrophysical Nuclear Reacting Flows Zhang, Xiaoyu Yi, Yuxiao Wang, Lile Xu, Zhi-Qin John Zhang, Tianhan Zhou, Yao Instrumentation and Methods for Astrophysics In astrophysical simulations, nuclear reacting flows pose computational challenges due to the stiffness of reaction networks. We introduce neural network-based surrogate models using the DeePODE framework to enhance simulation efficiency while maintaining accuracy and robustness. Our method replaces conventional stiff ODE solvers with deep learning models trained through evolutionary Monte Carlo sampling from zero-dimensional simulation data, ensuring generalization across varied thermonuclear and hydrodynamic conditions. Tested on 3-species and 13-species reaction networks, the models achieve $\lesssim 1\%$ accuracy relative to semi-implicit numerical solutions and deliver a $\sim 2.6\times$ speedup on CPUs. A temperature-thresholded deployment strategy ensures stability in extreme conditions, sustaining neural network utilization above 75\% in multi-dimensional simulations. These data-driven surrogates effectively mitigate stiffness constraints, offering a scalable approach for high-fidelity modeling of astrophysical nuclear reacting flows. |
| title | Deep Neural Networks for Modeling Astrophysical Nuclear Reacting Flows |
| topic | Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2504.14180 |