Emerging technologies like hypersonic aircraft, space exploration vehicles, and batteries avail fluid circulation in embedded microvasculatures for efficient thermal regulation. Modeling is vital during these engineered systems' design and operational phases. However, many challenges exist in developing a modeling framework. What is lacking is an accurate framework that (i) captures sharp jumps in the thermal flux across complex vasculature layouts, (ii) deals with oblique derivatives (involving tangential and normal components), (iii) handles nonlinearity because of radiative heat transfer, (iv) provides a high-speed forecast for real-time monitoring, and (v) facilitates robust inverse modeling. This paper addresses these challenges by availing the power of physics-informed neural networks (PINNs). We develop a fast, reliable, and accurate Scientific Machine Learning (SciML) framework for vascular-based thermal regulation -- called CoolPINNs: a PINNs-based modeling framework for active cooling. The proposed mesh-less framework elegantly overcomes all the mentioned challenges. The significance of the reported research is multi-fold. First, the framework is valuable for real-time monitoring of thermal regulatory systems because of rapid forecasting. Second, researchers can address complex thermoregulation designs inasmuch as the approach is mesh-less. Finally, the framework facilitates systematic parameter identification and inverse modeling studies, perhaps the current framework's most significant utility.

翻译：新兴技术如高超音速飞行器、太空探索飞行器及电池系统，通过嵌入微血管网络中的流体循环实现高效热调控。建模在工程系统的设计与运行阶段至关重要。然而，当前建模框架的开发仍面临诸多挑战。现有方法缺乏一个能够同时满足以下要求的精确框架：(i) 捕捉复杂血管布局中热通量的剧烈跃变，(ii) 处理斜向导数（涉及切向和法向分量），(iii) 应对辐射传热导致的非线性，(iv) 为实时监控提供高速预测，以及(v) 支持稳健的逆问题建模。本文利用物理信息神经网络（PINNs）的强大能力解决上述挑战。我们开发了一个快速、可靠且精确的基于血管热调控的科学机器学习（SciML）框架——CoolPINNs：一种基于PINNs的主动冷却建模框架。所提出的无网格框架优雅地克服了所有挑战。本研究的价值体现在多个方面：首先，该框架因具备快速预测能力，可用于热调控系统的实时监控；其次，无网格特性使研究者能够处理复杂的热调控设计问题；最后，该框架支持系统性的参数辨识与逆问题建模研究，这或许是当前框架最重要的应用价值。