Galaxy formation and evolution critically depend on understanding the complex
photo-chemical processes that govern the evolution and thermodynamics of the
InterStellar Medium (ISM). Computationally, solving chemistry is among the most
heavy tasks in cosmological and astrophysical simulations. The evolution of
such non-equilibrium photo-chemical network relies on implicit, precise,
computationally costly, ordinary differential equations (ODE) solvers. Here, we
aim at substituting such procedural solvers with fast, pre-trained, emulators
based on neural operators. We emulate a non-equilibrium chemical network up to
H$_2$ formation (9 species, 52 reactions) by adopting the DeepONet formalism,
i.e. by splitting the ODE solver operator that maps the initial conditions and
time evolution into a tensor product of two neural networks. We use
$\texttt{KROME}$ to generate a training set spanning $-2\leq
\log(n/\mathrm{cm}^{-3}) \leq 3.5$, $\log(20) \leq\log(T/\mathrm{K}) \leq 5.5$,
$-6 \leq \log(n_i/n) < 0$, and by adopting an incident radiation field
$\textbf{F}$ sampled in 10 energy bins with a continuity prior. We separately
train the solver for $T$ and each $n_i$ for $\simeq 4.34\,\rm GPUhrs$. Compared
with the reference solutions obtained by $\texttt{KROME}$ for single zone
models, the typical precision obtained is of order $10^{-2}$, i.e. the $10
\times$ better with a training that is $40 \times$ less costly with respect to
previous emulators which however considered only a fixed $\mathbf{F}$. The
present model achieves a speed-up of a factor of $128 \times$ with respect to
stiff ODE solvers. Our neural emulator represents a significant leap forward in
the modeling of ISM chemistry, offering a good balance of precision,
versatility, and computational efficiency.

星系的形成和演化取决于对控制星际介质（ISM）演化和热力学的复杂光化学过程的理解。在宇宙学和天体物理模拟中，解决化学问题是最繁重的任务之一。本研究通过采用基于神经算子的快速预训练模拟器，模拟了非平衡化学网络直到 H$_2$ 的形成，并取得了较高的精度、高效率的能力，从而在 ISM 化学模型的建模方面取得了重大进展。

使用神经算子模拟星际介质化学

Emulating the interstellar medium chemistry with neural operators

In this paper, we propose a generic algorithm to train machine learning-based
subgrid parametrizations online, i.e., with $\textit{a posteriori}$ loss
functions for non-differentiable numerical solvers. The proposed approach
leverage neural emulators to train an approximation of the reduced state-space
solver, which is then used to allows gradient propagation through temporal
integration steps. The algorithm is able to recover most of the benefit of
online strategies without having to compute the gradient of the original
solver. It is demonstrated that training the neural emulator and
parametrization components separately with respective loss quantities is
necessary in order to minimize the propagation of some approximation bias.

在本文中，我们提出了一种通用算法，用于在线训练基于机器学习的子网参数化，即使用非可微数值求解器的 $	extit {a posteriori}$ loss 函数。所提出的方法利用神经仿真器来训练减少状态空间求解器的近似，然后通过时间积分步骤传播梯度。该算法能够在不计算原始求解器的梯度的情况下恢复大部分在线策略的好处。实验证明，分别使用相应的损失量训练神经仿真器和参数化组件是必要的，以最小化某些近似偏差的传播。

基于神经模拟器的无梯度在线学习亚网尺度动力学

Gradient-free online learning of subgrid-scale dynamics with neural  emulators

We present CosmoPower-JAX, a JAX-based implementation of the CosmoPower
framework, which accelerates cosmological inference by building neural
emulators of cosmological power spectra. We show how, using the automatic
differentiation, batch evaluation and just-in-time compilation features of JAX,
and running the inference pipeline on graphics processing units (GPUs),
parameter estimation can be accelerated by orders of magnitude with advanced
gradient-based sampling techniques. These can be used to efficiently explore
high-dimensional parameter spaces, such as those needed for the analysis of
next-generation cosmological surveys. We showcase the accuracy and
computational efficiency of CosmoPower-JAX on two simulated Stage IV
configurations. We first consider a single survey performing a cosmic shear
analysis totalling 37 model parameters. We validate the contours derived with
CosmoPower-JAX and a Hamiltonian Monte Carlo sampler against those derived with
a nested sampler and without emulators, obtaining a speed-up factor of
$\mathcal{O}(10^3)$. We then consider a combination of three Stage IV surveys,
each performing a joint cosmic shear and galaxy clustering (3x2pt) analysis,
for a total of 157 model parameters. Even with such a high-dimensional
parameter space, CosmoPower-JAX provides converged posterior contours in 3
days, as opposed to the estimated 6 years required by standard methods.
CosmoPower-JAX is fully written in Python, and we make it publicly available to
help the cosmological community meet the accuracy requirements set by
next-generation surveys.

CosmoPower-JAX 是一种用于 - cosmological inferece - 的 JAX 实现。通过构建 - cosmological power spectra - 的神经仿真器，使用 JAX 的自动分化、批量评估和及时编译功能，并在 GPU 上运行推断管道，使用先进的基于梯度的采样技术加速参数估计数个数量级。

高维贝叶斯推断与可微宇宙学模拟器 ——CosmoPower-JAX

CosmoPower-JAX: high-dimensional Bayesian inference with differentiable  cosmological emulators

Neuromorphic engineering (NE) encompasses a diverse range of approaches to
information processing that are inspired by neurobiological systems, and this
feature distinguishes neuromorphic systems from conventional computing systems.
The brain has evolved over billions of years to solve difficult engineering
problems by using efficient, parallel, low-power computation. The goal of NE is
to design systems capable of brain-like computation. Numerous large-scale
neuromorphic projects have emerged recently. This interdisciplinary field was
listed among the top 10 technology breakthroughs of 2014 by the MIT Technology
Review and among the top 10 emerging technologies of 2015 by the World Economic
Forum. NE has two-way goals: one, a scientific goal to understand the
computational properties of biological neural systems by using models
implemented in integrated circuits (ICs); second, an engineering goal to
exploit the known properties of biological systems to design and implement
efficient devices for engineering applications. Building hardware neural
emulators can be extremely useful for simulating large-scale neural models to
explain how intelligent behavior arises in the brain. The principle advantages
of neuromorphic emulators are that they are highly energy efficient, parallel
and distributed, and require a small silicon area. Thus, compared to
conventional CPUs, these neuromorphic emulators are beneficial in many
engineering applications such as for the porting of deep learning algorithms
for various recognitions tasks. In this review article, we describe some of the
most significant neuromorphic spiking emulators, compare the different
architectures and approaches used by them, illustrate their advantages and
drawbacks, and highlight the capabilities that each can deliver to neural
modelers.

本论文综述了神经形态工程中的关键问题，包括信息处理、脑样计算、神经模型及其仿真，以及深度学习等。