CosmicRIM：通过结合可微模拟和递归推理机重建早期宇宙
CosmicRIM : Reconstructing Early Universe by Combining Differentiable Simulations with Recurrent Inference Machines
摘要：
在正演模拟框架下，从观测数据重建宇宙初始的高斯初始条件是宇宙学的一个主要挑战。这需要用昂贵的非线性正向模型来解决高维逆问题：宇宙学N体模拟。虽然棘手，直到最近，我们建议解决这个推理问题，使用自动可微N体求解器，结合递归网络学习推理方案，并获得宇宙初始条件的最大后验概率（MAP）估计。我们证明了使用现实的宇宙学观测值，学习推理是40倍的速度比传统的算法，如ADAM和LBFGS，这需要专门的退火方案，并获得更高质量的解决方案。
英文摘要：
Reconstructing the Gaussian initial conditions at the beginning of the Universe from the survey data in a forward modeling framework is a major challenge in cosmology. This requires solving a high dimensional inverse problem with an expensive, non-linear forward model: a cosmological N-body simulation. While intractable until recently, we propose to solve this inference problem using an automatically differentiable N-body solver, combined with a recurrent networks to learn the inference scheme and obtain the maximum-a-posteriori (MAP) estimate of the initial conditions of the Universe. We demonstrate using realistic cosmological observables that learnt inference is 40 times faster than traditional algorithms such as ADAM and LBFGS, which require specialized annealing schemes, and obtains solution of higher quality.
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