Nuclear Fuel

Nuclear fuel research focuses on understanding and optimizing fuel performance, encompassing material properties, behavior under irradiation, and efficient waste management. Current research employs advanced modeling techniques, including deep learning architectures like convolutional neural networks (CNNs), residual networks (ResNets), and Gaussian processes, to predict fuel characteristics (e.g., fission gas release, decay heat), analyze microstructures, and even optimize fuel designs. These data-driven approaches aim to improve the accuracy and efficiency of simulations, ultimately enhancing safety, sustainability, and economic viability of nuclear energy.