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:基于物理学知识的神经网络是一个将统计模式和领域知识结合起来构建预测模型的一致框架。基本思想是通过加入已知关系来丰富优化损失函数,以限制可能解的空间。流体动力学模拟是现代宇宙学的核心组成部分,而所需的计算既昂贵又耗时。与此同时,较快的暗物质模拟所需的资源较少,这导致了机器学习算法在实现各向异性差值填充(Baryon inpainting)方面取得进展,其中再现流体动力学模拟中的散度是一项持续的挑战。本文首次将基于物理学知识的神经网络应用于 Baryon inpainting。通过将神经网络架构的进展与物理约束相结合,将关于 Baryon 转换效率的理论注入到模型损失函数中。我们还引入了一种惩罚性预测比较方法,基于 Kullback-Leibler(KL)散度,以实现散度复制。通过同时提取 Simba 套件的完整 Baryonic 特性,我们的结果表明,基于暗物质晕的Baryonic预测的准确性得到了改善,成功恢复了基本的金属性关系,并恢复了跟踪目标模拟分布的散度。