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Emerging technologies like hypersonic aircraft, space exploration vehicles,and batteries avail fluid circulation in embedded microvasculatures forefficient thermal regulation. Modeling is vital during these engineeredsystems' design and operational phases. However, many challenges exist indeveloping a modeling framework. What is lacking is an accurate framework that(i) captures sharp jumps in the thermal flux across complex vasculaturelayouts, (ii) deals with oblique derivatives (involving tangential and normalcomponents), (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 byavailing the power of physics-informed neural networks (PINNs). We develop afast, reliable, and accurate Scientific Machine Learning (SciML) framework forvascular-based thermal regulation -- called CoolPINNs: a PINNs-based modelingframework for active cooling. The proposed mesh-less framework elegantlyovercomes all the mentioned challenges. The significance of the reportedresearch is multi-fold. First, the framework is valuable for real-timemonitoring of thermal regulatory systems because of rapid forecasting. Second,researchers can address complex thermoregulation designs inasmuch as theapproach is mesh-less. Finally, the framework facilitates systematic parameteridentification and inverse modeling studies, perhaps the current framework'smost significant utility.

CoolPINNs: A Physics-informed Neural Network Modeling of Active Cooling in Vascular Systems