Emulating Radiative Transfer in Astrophysical Environments
Rune Rost, Lorenzo Branca, Tobias Buck
辐射转移是天体物理学的一个基本过程,对于通过电离辐射和光子压力在模拟中解释观测和建模热和动态反馈至关重要。 然而,由于光与物质的复杂相互作用以及光速与天体物理环境中典型气体速度之间的差异,在数值上解决底层辐射传递方程是计算密集型的,因此在流体力学模拟中包括飞行辐射的影响特别昂贵。 这激励了替代模型的发展,可以显着加速辐射转移计算,同时保持高精度。 我们展示了基于傅里叶神经操作员架构与U-Nets相结合的替代模型。 我们的模型近似于时间依赖机制中的三维单色辐射转移,在吸收-排放近似中,实现超过2个数量级的加速,同时保持平均相对误差低于3%,证明了我们的方法有可能集成到最先进的流体动力学模拟中。
Radiative transfer is a fundamental process in astrophysics, essential for both interpreting observations and modeling thermal and dynamical feedback in simulations via ionizing radiation and photon pressure. However, numerically solving the underlying radiative transfer equation is computationally intensive due to the complex interaction of light with matter and the disparity between the speed of light and the typical gas velocities in astrophysical environments, making it particularly expensiv...