Based Aging

Based aging research focuses on modeling and predicting the effects of aging across diverse systems, from lithium-ion batteries to human faces and brains. Current efforts utilize machine learning, particularly deep learning architectures like diffusion models and graph neural networks, to generate realistic aging simulations and predict remaining lifespan or performance degradation. This work is significant for optimizing battery life, improving facial recognition systems' robustness to age changes, and advancing medical imaging analysis for early disease detection and personalized healthcare. The development of more accurate and diverse aging models is crucial for various applications, ranging from engineering to medicine.