  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.

Numerical Analysis;Machine Learning;Numerical Analysis