研究人员开发了一种新颖的混合解析-物理信息神经网络（PINN）模型，用于模拟地热系统中的地下传热。该方法将解析解与PINN相结合，以有效处理异质土壤条件和钻孔换热器的复杂性。该模型通过专注于对理想化均匀近似的学习修正，有效地消除了奇点问题并捕获了整体传热，在各种解析模型中均显示出有效性。 AI
影响 这种混合模型可以提高地热能源系统的模拟效率和准确性，可能加速其开发和部署。
排序理由 该集群包含一篇在arXiv上发表的关于新建模技术的学术论文。
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arXiv:2607.12271v1 Announce Type: new Abstract: In this paper, a parametric physics-informed neural network for solving the heterogeneous soil thermal problem with borehole heat exchangers (BHEs) as singular sources is developed. There are three novel features in the present fram…
In this paper, a parametric physics-informed neural network for solving the heterogeneous soil thermal problem with borehole heat exchangers (BHEs) as singular sources is developed. There are three novel features in the present framework; namely, (i) the singularity is naturally …