Temperature Field

Temperature field reconstruction aims to accurately determine temperature distributions within a system, often from sparse or noisy sensor data. Current research heavily utilizes machine learning, particularly physics-informed neural networks (PINNs) and convolutional neural networks (CNNs), often incorporating techniques like transfer learning and multi-fidelity modeling to improve accuracy and efficiency, especially when dealing with complex geometries or limited high-fidelity data. These advancements are crucial for optimizing sensor placement, improving the accuracy of simulations in various fields (e.g., additive manufacturing, aerospace engineering), and enabling real-time monitoring and control of systems where precise temperature knowledge is critical. The development of robust and efficient methods for temperature field reconstruction has significant implications for diverse applications ranging from industrial process optimization to medical imaging.