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、太空探索运载器及电池）依赖嵌入式微血管系统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，但当前建模框架面临诸多挑战：缺乏能够同时满足以下条件的精确框架：（i）捕捉复杂血管布局中热通量的急剧跃变；（ii）处理涉及切向和法向分量的斜向导数；（iii）解决辐射传热导致的非线性问题；（iv）为实时监测提供快速预测；（v）支持稳健的逆向建模。本文通过利用物理信息神经网络（PINNs）解决上述挑战，开发了一种快速、可靠且精确的基于血管热调控的科学机器学习（SciML）框架——CoolPINNs：面向主动冷却的PINNs建模框架。该无网格框架优雅地克服了所有上述难题。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测；其次，由于采用无网格方法，研究者可处理复杂的热调节设计；最后，该框架支持系统化的参数辨识与逆向建模研究——这或许是当前框架最重要的应用价值。