Kavli Affiliate: Lile Wang
| First 5 Authors: Xiaoyu Zhang, Yuxiao Yi, Lile Wang, Zhi-Qin John Xu, Tianhan Zhang
| Summary:
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 $sim 2.6times$ 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.
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