Physics-informed neural networks have emerged as a coherent framework for building predictive models that combine statistical patterns with domain knowledge. The underlying notion is to enrich the optimization loss function with known relationships to constrain the space of possible solutions. Hydrodynamic simulations are a core constituent of modern cosmology, while the required computations are both expensive and time-consuming. At the same time, the comparatively fast simulation of dark matter requires fewer resources, which has led to the emergence of machine learning algorithms for baryon inpainting as an active area of research; here, recreating the scatter found in hydrodynamic simulations is an ongoing challenge. This paper presents the first application of physics-informed neural networks to baryon inpainting by combining advances in neural network architectures with physical constraints, injecting theory on baryon conversion efficiency into the model loss function. We also introduce a punitive prediction comparison based on the Kullback-Leibler divergence, which enforces scatter reproduction. By simultaneously extracting the complete set of baryonic properties for the Simba suite of cosmological simulations, our results demonstrate improved accuracy of baryonic predictions based on dark matter halo properties, successful recovery of the fundamental metallicity relation, and retrieve scatter that traces the target simulation's distribution.

翻译：物理信息神经网络已成为一种构建预测模型的统一框架，该框架能够将统计模式与领域知识相结合。其核心理念是通过已知关系丰富优化损失函数，以约束可能的解空间。流体动力学模拟是现代宇宙学的核心组成部分，但所需计算既昂贵又耗时。与此同时，相对较快的暗物质模拟所需资源较少，这促使机器学习算法在重子填充领域成为活跃研究方向；在此过程中，重现流体动力学模拟中的离散度仍是一项持续挑战。本文首次将物理信息神经网络应用于重子填充，通过结合神经网络架构的进展与物理约束，将重子转换效率理论注入模型损失函数。我们还引入了一种基于库尔贝克-莱布勒散度的惩罚性预测比较方法，以强制实现离散度的复现。通过同时提取Simba宇宙学模拟套件中完整的重子属性集合，我们的结果表明：基于暗物质晕属性的重子预测精度得到提升，成功恢复了基本金属丰度关系，并获取了能够追踪目标模拟分布的离散度。