Subsurface Temperature

Subsurface temperature research focuses on accurately mapping and predicting temperatures beneath the Earth's surface and within various media, driven by needs ranging from resource exploration to medical procedures. Current research employs diverse approaches, including physics-informed neural networks (like graph neural networks and convolutional neural networks) for spatial interpolation and prediction, and adaptive observer frameworks for real-time estimation in dynamic environments like electrosurgery. These advancements improve the accuracy and efficiency of subsurface temperature modeling, impacting fields such as geothermal energy, groundwater management, and minimally invasive surgery by providing more precise and readily available data.